Skip to main content

Full text of "Internet adoption in post-communist countries : a proposed model for the study of internet diffusion"

See other formats


INTERNET ADOPTION IN POST-COMMUNIST COUNTRIES: 
A PROPOSED MODEL FOR THE STUDY OF INTERNET DIFFUSION 



By 



DAN1ELA V. DIMITROVA 



A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL 

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT 

OF THE REQUIREMENTS FOR THE DEGREE OF 

DOCTOR OF PHILOSOPHY 

UNrvERsnr of Florida 

2003 



ACKNOWLEDGMENTS 

I greatly appreciate the guidance and support of Dr. Sylvia Chan-Olmsted, my 
dissertation chair. Thanks are also due to all of my other committee members. Professor 
Wayne Wanta, now at the University of Missouri, was instrumental to the beginning of 
this project, and his guidance with the requisite job search at the end was invaluable. Dr. 
Kurt Kent was always available for help and advice, both related and unrelated to my 
dissertation research. Professor Mindy McAdams believed in my success and contributed 
to my development as a teacher and scholar. Last but not least, I want to thank my 
external member, Dr. Rich Beilock, who led me to my dissertation idea and also kept me 
on my toes, helped with the data collection, offered valuable methodological assistance, 
all with a sense of humor. 

I thank my mentor, Dr. Lynda Lee Kaid, who was always there for me and whose 
passion for research served as a wonderful example. 

I would like to thank my parents, Velitcka Ivanova Boytcheva and Vesselin 
Dimitrov Boytchev, for their endless support and encouragement in all my endeavors. 
Their love for learning and belief in my success is my deepest inspiration. 

Thanks go to all my dear friends here in Gainesville and around the world. 

Finally, I wish to thank Alexander, who firmly stood by me all the way through this 
challenging process. His love and support are invaluable. 



11 



TABLE OF CONTENTS 

Page 

ACKNOWLEDGMENTS ii 

LIST OF TABLES vi 

LIST OF FIGURES vii 

ABSTRACT viii 

1 INTRODUCTION 1 

Internet Significance 1 

Economic Contributions 2 

Political Contributions 3 

Technological Contributions 4 

Social Contributions 5 

Other Contributions 6 

Post-communist Countries 7 

Transition Progress 7 

Geographic Regions 9 

Economic Inequalities 9 

Need for Research of Internet Diffusion 10 

Research Method 16 

Dissertation Outline 17 

2 INTERNET AND SOCIETY 18 

Development of the Internet 18 

The Invention of the Internet 18 

Internet Growth and Global Expansion 21 

Internet and Political Development 25 

Democracy and the Internet 26 

Free Press and the Internet 29 

Internet and Economic Development 31 

Lower Production and Distribution Costs 32 

The Internet Economy 33 

Global Markets 34 

Leapfrogging 35 



in 



3 LITERATURE REVIEW 37 

Diffusion of Innovations 37 

Basic Generalizations 38 

Technology Innovation Attributes 39 

Cluster Innovations 42 

Types of Adopters 43 

Other Communication Technologies 46 

Levels of Internet Adoption 48 

Other Considerations for Internet Adoption 49 

New Media Technologies Research 50 

Economic Factors 51 

Political Climate and Policy 57 

Technology/Infrastructure 66 

Audience Characteristics 69 

Cultural Factors 72 

Conceptual Framework 76 

Further Thought 77 

4 METHODS 82 

Research Design 82 

Data Collection 84 

Operational Definitions 85 

Economic variable 85 

Political climate and policy variables 86 

Technology/Infrastructure variable 88 

Audience variables 89 

Cultural variable 90 

Dependent variable 90 

The Model 95 

Statistical Procedures 96 

Multiple Regression Technique 96 

Stepwise Regression 99 

Hypotheses 100 

Proposition 1 100 

Proposition 2 100 

Proposition 3 101 

Proposition 4 101 

Proposition 5 102 

Proposition 6 102 

Methodological Notes 102 

5 RESULTS 107 

Descriptive Analysis 107 

Internet Users 107 



IV 



Gross National Product 109 

Democratization HO 

Telecommunications Privatization 1 1 1 

Teledensity "* 

Education 112 

Religion 112 

Bivariate Correlations 112 

Regression Results 113 

Statistical Assumptions 113 

Hypotheses Testing 115 

Final Model 119 

Tobit Estimates 124 

6 DISCUSSION 126 

Overview 126 

Discussion of Descriptive Analysis 128 

Regional Variations 128 

Growth of Internet Use 130 

Discussion of Hypotheses 2 through 6 134 

National Income 134 

Democratization 136 

Telecommunications Privatization 138 

Infrastructure 140 

Education 142 

Religion 144 

Refined Conceptual Framework 147 

7 CONCLUSION 151 

Conclusions 151 

Implications 153 

Theoretical Implications 153 

Applied Implications 155 

Limitations 159 

Validity 160 

Internal validity 160 

External validity 163 

Reliability 164 

Suggestions for Future Research 165 

LIST OF REFERENCES 169 

BIOGRAPHICAL SKETCH 184 



LIST OF TABLES 
Table page 

1-1. Internet Hosts per 10,000 population in 1995 and 1999 15 

4-1. Definition of variables in the proposed model of Internet diffusion 83 

4-2. Correlation matrix for the continuous independent variables 105 

5-1. Internet users per 10,000 people in 1999 109 

5-2. Descriptive statistics of variables 1 10 

5-3. Pearson correlations between dependent and independent variables 113 

5-4. Regression results for Internet users 1 16 

5-5. Summary of hypothesis testing 118 

5-6. ANOVA Table for Complete Model 120 

5-7. ANOVA Table for Model 2 121 

5-8. ANOVA Table for Model 3 122 

5-9. ANOVA Table for Model 4 123 

5-10. Tobit estimates for final model 125 



VI 












LIST OF FIGURES 
Figure E^ge 

1-1. Internet hosts across world regions 12 

2-1. Internet timeline 21 

2-2. Total number of Internet hosts 22 

3-1. Internet hosts across income regions 53 

4-1. Graphic model 95 

5-1. Distribution of Internet users across countries 1 14 



vn 



Abstract of Dissertation Presented to the Graduate School 

of the University of Florida in Partial Fulfillment of the 

Requirements for the Degree of Doctor of Philosophy 

INTERNET ADOPTION IN POST-COMMUNIST COUNTRIES: 
A PROPOSED MODEL FOR THE STUDY OF INTERNET DIFFUSION 

By 

DANIELA V. DIMITROVA 

May 2003 

Chair: Dr. Sylvia Chan-Olmsted 

Major Department: Mass Communication 

This dissertation proposed and tested a five-dimensional theoretical framework to 
explain the variations in Internet use across the post-communist countries. The 
framework included economic, political climate and policy, technology/infrastructure, 
cultural, and audience factors. Three factors emerged as critically important: economic, 
political, and infrastructure factors. Cultural factors seemed to have partial impact. These 
findings suggest that the traditional country-level indicators of economic wealth and 
technological infrastructure remain important determinants of Internet use in the 
countries of Eastern Europe and the former Soviet Union. The most significant 
determinant, however, was level of democratization. 

The results of the multiple regression analysis reported in this study indicate that 
democratization, teledensity, and GNP per capita were the three most important factors 
positively related to Internet use in the post-communist countries. Being predominantly 
Muslim had a negative effect on Internet use while being Western Christian (Protestant or 



via 






Catholic) seemed unrelated to Internet adoption. Neither length of telecommunications 
privatization nor education level appeared significant in this analysis. 

Thus, the results of this study shed light on the macro-level indicators that affect 
Internet adoption in the post-communist countries. These have important implications for 
policy makers at the local, national, and international level. The proposed Internet 
diffusion model may be applicable to other regions, but this analysis focused only on the 
28 post-communist countries. 



ix 






CHAPTER 1 
INTRODUCTION 

Human history has witnessed the rise and fall of many new technologies. The 
Internet is often described as the most revolutionary new technology ever and its growth 
in every country around the globe is remarkable as well as irreversible. The Internet is not 
just a technological innovation; it is a unique, fascinating, multifaceted network that 
transcends national boundaries. It affects people, communities, organizations, and 
countries around the world. In this dissertation, Internet adoption is examined in a unique 
region of the world-the post-communist countries. 1 The dissertation proposes a five- 
dimensional analytical framework to account for differences in Internet diffusion in those 
countries. Testing the five-dimensional framework identifies the main determinants of 
Internet adoption and illustrates how these determinants affect Internet use in the post- 
communist countries. 

Internet Significance 

The Internet constitutes "at once a world-wide broadcasting capability, a 
mechanism for information dissemination, and a medium for collaboration and 
interaction between individuals and their computers without regard for geographical 
location" (Leiner et al., 1997). The Internet has affected society in a variety of ways as a 



1 The post-communist countries are defined as the countries of Eastern Europe, the 
former Soviet Union, and Mongolia. Specifically, the study examines Albania, Armenia, 
Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, 
Estonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Macedonia 
(FYROM), Moldova, Mongolia, Poland, Romania, Russia, Slovak Republic, Slovenia, 
Tajikistan, Turkmenistan, Ukraine, Uzbekistan, and Yugoslavia (Serbia and 
Montenegro). 



result of its multidimensional functions (Castells, 1996; ITU, 1999; Newhagen & Rafaeli, 
1996; OECD, 1998b). One of the most fascinating aspects of Internet diffusion is its 
impact on a global scale. The Internet has exercised a tremendous impact on the 
economic, political, technological, and social development of countries (IMF, 2000a; 
Mitchell, 1995; World Bank, 2001). The positive contributions of Internet adoption in 
each of these four areas are reviewed below. 

Economic Contributions 

One of the more compelling arguments made to encourage global Internet 
diffusion is that countries (developing countries in particular) can improve their 
economic status with the adoption of this technology (Hanson & Narula, 1990). Clearly, 
the Internet affects the economic situation in a country as it facilitates international trade, 
lowers production and distribution costs, optimizes productivity within and between 
companies, and offers leapfrogging possibilities for less developed countries. 

The Internet facilitates international trade and thus allows nations to increase 
exports and imports of goods to and from other countries (The new economy, 2000). It 
allows better integration of national markets both internally and 
externally/internationally, and also increases possibilities for economic decentralization 
(Maddock, 1997). With the improvement of Internet security, e-commerce is expected to 
increase and more national exports and imports will be likely (DePrince & Ford, 1999). 

In addition, the Internet lowers production and distribution costs (DePrince & 
Ford, 1999; Guthrie & Austin, 1996; The new economy, 2000). These lower costs can 
have a positive effect on the internal economic situation. The Internet intensifies price 
competition among producers, which leads to lower prices for consumers (Guthrie & 



Austin, 1996). Labor productivity is increased, and searching, distribution, and 
transaction costs tend to drop (DePrince & Ford, 1999). 

It has been argued that the Internet optimizes productivity within and between 
companies (Maddock, 1997; Malecki, 1997, 2000). The development of a better 
telecommunications infrastructure in general, Maddock (1997) argues, increases 
productivity and competitiveness of local companies. In addition, telecommunications 
development facilitates economic growth by increasing market efficiency (Maddock, 
1997). Finally, better telecommunications can improve management within corporations 
(Daly & Miller, 1998; Maddock, 1997). 

Another economic impact of higher Internet penetration is the leapfrogging effect. 
Leapfrogging is the ability of countries that are technologically behind suddenly to skip 
generations of intermediate technology and adopt the latest one. The adoption of the 
latest technology is seen as beneficial to countries as they can succeed in catching up 
economically with more technologically advanced societies (Singh, 1999). 

Political Contributions 

In addition to the economic benefits, the Internet can serve as a tool for enhancing 
democratic governance worldwide. A number of scholars have discussed how the Internet 
can affect the political situation within a country (Ahmann, 1998; Godwin, 1998; Poster, 
1995). From the early days of the ARPANET (Advanced Research Projects Agency 
Network), people envisioned the Internet's expansion to a worldwide, borderless network 
(Rogerson & Thomas, 1998). This global network can strengthen democracy in at least 
two ways: first, people can stay better informed and thus can make better choices; 
second, the Internet offers citizens a global forum for free expression and exchange of 
ideas (Perrit, 1999; Poster, 1995, 2001). 



The Internet provides citizens in any country with the opportunity to stay better 
informed and thus learn more about options for political action and democratic 
governance. The information available online is rich and is difficult to censor. The 
sources of information are also numerous, ranging from established media corporations to 
independent journalists to regular citizens publishing online. People can find an 
enormous amount of information on any topic that interests them. Better informed 
citizens, arguably, can make better decisions in society and more informed political 
choices. 

Another way in which the Internet can strengthen democracy on a global level is 
by offering citizens a forum for free expression and exchange of ideas with like-minded 
people. In most countries, people are free to go online and express their views to a global 
audience. They can also search for and communicate with like-minded people around the 
globe. Such online communication and uncensored expression reaches a large number of 
people and extends individual freedom. It also allows citizens to organize collective 
action and thus influence public policy. 

It is important to note that control of the Internet by one government or corporation 
is unlikely. Even though regulation can limit the use of the Internet to some degree, it is 
quite difficult to enforce such regulation on a global scale. 

Technological Contributions 

Furthermore, the Internet brings technological growth to a country as it can 
strengthen its overall telecommunications development. Maddock (1997) argues that 
telecommunication causes development progress in several ways. First, it leads to the 
creation of at least one leading sector of the economy in the country. Second, it 
accelerates diffusion of other technologies and thus allows faster catch-up for less 



developed countries. In today's day and age, it is hardly questionable that the Internet is 
critical for the technological advancement of a nation and has become an indispensable 
part of the modern telecommunications infrastructure. 

Social Contributions 

The Internet also brings about social change. With the advent of the Internet, we 
may be coming closer to what Marshall McLuhan conceived as "the global village" 
(McLuhan & Powers, 1989). This is another reason why it is important to research 
Internet diffusion. The emergence of a global community is facilitated by the Internet. 
People from all nationalities and various backgrounds can form communities online. 
Boundaries and distances between countries shrink in the virtual world. Thus, the Internet 
(through email, chats, and bulletin boards) brings people closer, regardless of geographic 
location. These online applications redefine social relationships within countries. The 
effect of virtual communities can increase since email remains one of the most popular 
Internet activities. 

Winner (1997) discusses the idea that technological innovations lead to social, 
cultural, and political transformation. He argues that "technical innovations of any 
substantial extent involve a reweaving of the fabric of society, a reshaping of some of the 
roles, rules, and relationships that comprise our ways of living together." New 
technologies, then, should be studied closely and not only at one point in time. Their 
effect on the relationships in society should also be examined and their long-term impact 
followed, if we are to understand adequately the whole diffusion process and its 
consequences. 



Other Contributions 

In addition to the benefits noted above, the global expansion of the Internet is 

beneficial to countries in the areas of education and health (ITU, 1999; World Bank, 

2001). The Internet as an unlimited and readily accessible information resource can be 

useful for data retrieval on a global level (Sadowsky, 1993). It can also facilitate better 

service delivery and learning through distance education (World Bank, 2001). 

Finally, intellectual curiosity makes it a worthwhile effort to study the expansion of 

the global network of networks. Press has put it well (1997): 

Tracking the diffusion of the Internet is a daunting task because it is growing 
rapidly, is global, and expands organically, at the edges and internally, without 
central control. Still, business people, policy makers, and capacity planners are 
better off with approximate data than none at all. . . . Over the years, busy humanity 
has covered the globe with cities linked by railroads, highways, telephone lines, 
power grids, canals, and so forth, and we are now weaving digital communication 
links-the nervous system. I suspect that curiosity and aesthetics motivate the 
people tracking the global diffusion of the Internet as much as profit. 

In this dissertation, the challenging task of tracking and explaining Internet 
adoption in a unique region of the world is pursued. The study examines Internet 
adoption in the countries of Eastern Europe and the former Soviet Union and analyzes the 
multivariate relationships to explain their levels of adoption. The dissertation proposes a 
five-dimensional conceptual framework to account for the differences in Internet 
diffusion in the post-communist countries. The study draws on research from the United 
States, Western Europe, and Latin America as well as cross-country Internet diffusion 
literature. It proposes and tests a comprehensive five-dimensional theoretical model. 
Based on the results, a refined conceptual framework is retrieved. Discussion shows how 
that refined model can be employed in predicting and modifying future Internet adoption 
at the country level. The rest of this chapter briefly describes the post-communist 









countries and then outlines the significance of region-specific research on Internet 
diffusion. 

Post-communist Countries 
The post-communist countries examined in this research include the former Soviet 
Union republics, Eastern Europe, and Mongolia. Specifically, they include the following 
28 nations: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, 
Croatia, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia, 
Lithuania, Macedonia (FYROM), Moldova, Mongolia, Poland, Romania, Russia, Slovak 
Republic, Slovenia, Tajikistan, Turkmenistan, Ukraine, Uzbekistan, and Yugoslavia 
(Serbia and Montenegro). These countries represent a unique region of the world. Since 
the end of the Cold War they have been undergoing a transition from totalitarian regimes 
to democratic societies. A Freedom House report titled Nations in Transit examines the 
transition in the region, which is characterized by trends toward building civil society and 
market economy (Karatnycky et al., 1997). The report shows that the post-communist 
countries can be divided into three distinct groups, based on progress in these two areas 
(Karatnycky et al., 1997). The members of the three groups are listed below. 

Transition Progress 

The first group includes the leaders in the transition process: Poland, the Czech 
Republic, Hungary, Slovenia, Estonia, Latvia and Lithuania. These countries are 
classified by the Freedom House based on their 1996 survey as "liberal democracies, or 
polities that are well on their way to democracy, with vibrant civil societies, well- 
established rule of law, and market economies" (Karatnycky et al., 1997, 17). These six 
nations, no doubt, have made considerable progress and are leaders in the transition 
process in the region. 



8 

The next group of post-communist countries was classified as the intermediary 
group. It consists of countries that have made some progress towards the goal of building 
strong democratic societies, but the transition has not been as quick or smooth as in the 
leading countries noted above. Albania, Armenia, Bulgaria, Croatia, Georgia, 
Kyrgyzstan, Macedonia (FYROM), Moldova, Romania, Russia, the Slovak Republic, and 
Ukraine are members of the intermediary group. Bosnia and Herzegovina and Yugoslavia 
(Serbia and Montenegro) are also members of this group. There is considerable variation 
across these countries, both in terms of political development and economic progress. 

The third group among the post-communist countries consists of those that have 
been slow to change, relative to the rest of the transition societies- Azerbaijan, Belarus, 
Kazakhstan, Tajikistan, Turkmenistan, and Uzbekistan. The Freedom House report posits 
that the first and the third group are more stable while the middle group is less stable as 
countries belonging to that group are more fluid and can transition to the next levels 
rather quickly (Karatnycky et al., 1997). However, the three groups are distinctly 
different from each other and are likely to remain different over time. In other words, the 
distinctive features of the three groups of transitional societies are stable. An interesting 
observation is that the countries in the three groups are clustered together geographically. 
Also, "the intermediate countries deserve particular attention, as their variable legacies 
and half-hearted reforms do not imply any clear-cut outcomes" (Karatnycky et al., 1997, 
20). 

Another grouping of the countries in the region is given by the United Nations 
Development Programme (UNDP, 1999), which uses six regional groupings in its Human 



Development Report for Central and Eastern Europe and the Commonwealth of 
Independent States (CIS), based primarily on geographic location. 

Geographic Regions 

The post-communist countries can be divided into 6 regions based on their 
geographic location: Central Europe, Eastern Europe, Caucasus, Baltic states, Western 
Former Soviet Union, and Central Asia. 

Probably the most advanced group of countries is located in Central Europe. This 
group includes the following states: the Czech Republic, Hungary, Poland, the Slovak 
Republic, and Slovenia. The second geographic group is Eastern Europe. The Eastern 
European countries are Bulgaria, Croatia, Macedonia (FYROM), Slovenia, Romania, and 
Yugoslavia (Serbia and Montenegro). The Caucasus region includes Armenia, 
Azerbaijan, and Georgia. All of these counties are former Soviet republics. The Baltic 
states have also made considerable strides in the post-Cold War transition. These states 
include Estonia, Latvia, and Lithuania. The Western FSU (Former Soviet Union) 
incorporates the following states: Belarus, Moldova, Russia, and Ukraine. Finally, 
Central Asia includes the republics of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan 
and Uzbekistan. 
Economic Inequalities 

Using national income as a criterion, the post-communist countries can be divided 
into four groups, according to the 2000 World Development Report of the World Bank, 
which is based on 1998 data. The four groups are low income (less than $760 GNP per 
capita), lower middle income ($761 -$3,030), upper middle income ($3,031 -$9,360), and 
high income ($9,361 or more). 



10 

The low income countries in the region are Albania, Armenia, Azerbaijan, Bosnia 
and Herzegovina, Kyrgyzstan, Moldova, Mongolia, Tajikistan and Turkmenistan. The 
next category-lower middle income-includes the majority of the countries: Belarus, 
Bulgaria, Georgia, Kazakhstan, Latvia, Lithuania, Macedonia (FYROM), Romania, 
Russia, Ukraine, Uzbekistan, and Yugoslavia (Serbia and Montenegro). Upper middle 
income states are Croatia, the Czech Republic, Estonia, Hungary, Poland, and the Slovak 
Republic. The only high income country in the region is Slovenia (World Bank, 2000). 
In conclusion, the post-communist countries have made different progress in the 
post-Cold War transitional period (de Melo & Gelb, 1996). Variations both in political 
and economic development exist (EBRD, 1997). 
Need for Research of Internet Diffusion 

Understanding the process of Internet adoption at the county level is, first and 
foremost, critical for formulating public policy. National policies can contribute to 
accelerated Internet adoption, which can be beneficial to the country. As noted earlier, the 
Internet can contribute to national development in several ways. Internet adoption has a 
positive economic impact overall. The utility of the Internet as a political tool, which 
allows people to stay better informed and participate more fully in the political processes, 
has also been discussed. Country-specific research on Internet diffusion can also be used 
to enhance the technological development of the country, as outlined above. Finally, the 
Internet has important social functions, as it allows individuals to create online 
communities and interact with people around the globe, regardless of geographic 
boundaries. The Internet deeply impacts societies. Therefore, it is critical to better 
understand the process of its adoption. This will enable policy makers to exploit the 
Internet's full capabilities (Maherzi, 1997; Sadowsky, 1993; World Bank, 2001). 



11 

The dissertation focuses on the aggregate level of Internet adoption. Various 
studies in multiple disciplines have examined the topic of Internet diffusion at the 
individual level. Most research has been conducted in the industrialized nations. Research 
on Internet adoption and diffusion in the United States in particular has been quite 
extensive (Atkin et al., 1998; GVU, 1998; Lin, 1999; Lindstrom, 1997; NTIA, 1995. 
1998, 1999; Pew, 1995; USIC, 2000) and the number of studies looking at Internet 
adoption and usage keeps growing. 

The focus of Internet studies in general has been primarily on the United States 
(Daly & Miller, 1998). Literature exists also on Internet adoption by other industrialized 
nations, such as The Group of Eight (G-8) nations (Canada, France, Germany, Italy, 
Japan, Russia, United Kingdom, and United States) and OECD member countries 
(Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, 
Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Mexico, 
Netherlands, New Zealand, Norway, Poland, Portugal, Spain, Sweden, Switzerland. 
Turkey, United Kingdom, and United States) (Hargittai, 1999; McElhinney, 2001). Yet 
few studies have examined the process of Internet adoption in other countries. 

As the birthplace of the global information superhighway, the United States 
remains one of the countries with the highest Internet penetration (ITU, 1999; Pitkow, 
1996; USCD, 1998; USIC, 2000). Other developed countries (notably Scandinavian 
nations) are also leaders in Internet adoption. Gunarante (2001) identified three "global 
centers" where information technology is concentrated: NAFTA center, EU center, and 
Asian-Pacific center. Countries that do not belong to any of those centers are considered 
on the periphery of the Information Society (Gunarante, 2001). 



12 

The global distribution of Internet hosts shows that more than 88 percent of the 
hosts in 1999 were located in North America and Europe (ITU, 1999). The members of 
the European Union clearly have higher Internet penetration than the rest of the world. As 
Figure 1 illustrates, South Asia, North Africa, the Middle East, and sub-Saharan Africa 
exhibit lower levels of Internet adoption (ITU, 1999; World Bank, 2000). The countries 
where extensive Internet research exists tend to have higher Internet penetration. Figure 
1-1 shows the unequal distribution of Internet hosts in different regions of the world. 

Internet Hosts by Region 




South Asia Middle East* North Sub-Saharan Africa East Asia & Pacific Latin America & Europe & Central Asia European Union 

Africa Caribbean 

Figure 1-1. Internet hosts across world regions. Source: World Bank, 2000. 

The obvious discrepancy between the industrialized countries and the rest of the 
world makes it not only interesting but also critical to study the determinants of Internet 
diffusion. It is very important to examine whether and how the "global information 
highway" is adopted by other countries, especially considering the possibility that the 
Internet not only bridges, but also widens the gap between rich and poor countries (Tele- 



13 






haves, 1996; WIPO, 2001). Furthermore, cross-cultural studies allow for identifying 
regional differences in the adoption of information technologies. 

Studies of Internet adoption at the country level will not only provide insights about 
the process of Internet diffusion across countries but also show whether this process 
varies by certain country-level characteristics. Our understanding of Internet diffusion in 
non-Western countries is still very limited. As shown above, differences in levels of 
Internet penetration between industrialized countries and the rest of the world are quite 
significant. These differences cannot be fully explained by the existing literature. This 
dissertation extends current literature by offering insights on Internet trends in the post- 
communist countries. 

Better understanding of how new technologies are adopted by post-communist 
countries can help development in those countries. Even though the direct causal 
relationship between Internet usage and development/economic growth has been debated, 
studies have shown consistently a strong positive correlation between telecommunication 
services and country-level economic indicators such as per capita income (Arnum & 
Conti, 1998; Elie, 1998; Hargittai, 1999; Singh, 1999). The process of Internet adoption 
remains not fully understood. It has become clear that per capita income plays an 
important role, but few studies have examined what other factors affect Internet adoption 
at the macro/societal level. This study adds to current literature as it proposes a more 
comprehensive model of Internet diffusion. 

This dissertation focuses on Internet adoption in the former Soviet bloc. The post- 
communist countries are of particular interest for several reasons. First, they present a 
unique case study: countries that were subjected to communist rule for 40 to 70 years 



14 

give researchers a chance to follow the transition of telecommunications in post- 
communist societies (Katchanovski, 2000). As Rose (2002, 33) notes, "Internet access is 
especially important in the transition countries, because the transition process is about 
opening up a country to the world." 

The post-communist countries share at least 40 years of Soviet influence, which 
makes them a different cluster for research. They share certain objective characteristics, 
such as high literacy rates and high educational levels (UNDP, 1999). At the same time, 
they differ in some cultural aspects, such as religious beliefs and historical experience 
(Katchanovski, 2000). The focus on one particular region of the world allows researchers 
to detect more intricate relationships among variables and gain insights about the 
magnitude of importance of country-level characteristics. 

The post-communist countries present the opportunity for a case study of the 
Internet adoption process among a group of relatively similar countries. It is interesting to 
examine whether and how countries that had relatively similar technological, political, 
and economic levels have reached significant differences in the area of new information 
and communications technologies, such as the Internet. The post-communist countries 
can either follow the models of Internet adoption of other countries—the industrialized 
countries, for instance—or exhibit a different path of Internet adoption due to their unique 
socio-economic development. The results of the study advance knowledge of different 
patterns of global Internet diffusion. 

Further research on Internet adoption in the region is needed because it has been 
identified as the next area where an Internet boom will be seen (Arnum & Conti, 1998; 



15 



ITU, 1999; USIC, 2000). This upward trend makes research on Internet adoption in the 
post-communist countries even more timely and important. 
Table 1-1. Internet Hosts per 10,000 population in 1995 and 1999. 



COUNTRY NAME 


Hosts in 1995 


Hosts in 1999 


Increase (%) 


Albania 


0.11 


0.24 


118 


Armenia 


0.46 


1.85 


302 


Azerbaijan 


0.02 


0.23 


1,050 


Belarus 


0.02 


0.79 


3,850 


Bosnia and Herzegovina 





1.38 


N/A 


Bulgaria 


1.26 


11.9 


844 


Croatia 


5.27 


25.94 


392 


Czech Republic 


21.16 


85.59 


304 


Estonia 


24.11 


174.66 


624 


Georgia 


0.11 


1.59 


1,345 


Hungary 


15.44 


93.13 


503 


Kazakhstan 


0.12 


1.47 


1,350 


Kyrgyz Republic 





4.03 


N/A 


Latvia 


5.25 


50.83 


868 


Lithuania 


1.23 


30.45 


2,376 


Macedonia (FYR) 


0.46 


4.4 


857 


Moldova 


0.01 


2.42 


241 


Mongolia 





0.05 


N/A 


Poland 


5.98 


40.9 


584 


Romania 


0.77 


9.01 


1,070 


Russian Federation 


1.48 


13.09 


784 


Slovak Republic 


5.61 


38.79 


591 


Slovenia 


28.22 


99.12 


251 


Tajikistan 





0.24 


N/A 


Turkmenistan 





0.56 


N/A 


Ukraine 


0.47 


4.56 


870 


Uzbekistan 


0.02 


0.05 


150 


Yugoslavia (Serbia & Montenegro) 


N/A 


7.65 


N/A 



Source: World Bank, 2000. 

internet use in the post-communist countries has increased exponentially over the 
past decade (ITU, 1999; Magyar & Karvalics, 2001). Table 1-1 illustrates the growth of 
Internet usage in the post-communist countries by showing the increase in the number of 
Internet hosts. From 1995 to 1999, that number has doubled at the least. In the case of 



16 

Belarus, for example, Internet hosts increased by 3,850 percent. Lithuania, for example, 
had only 1.23 hosts per 10,000 in 1995, but the number increased 2,376 percent to 30.45 
hosts per 10,000 in 1999. Bulgaria had a similar number of hosts in 1995-1.26 per 
10,000-but the increase in 1999 was relatively small compared with Lithuania-to 1 1.9 
hosts only. These examples illustrate that the number of Internet hosts per capita in post- 
communist countries remains uneven and the growth rate across countries varies widely. 

Among the Central and Eastern European nations, Slovenia, the Czech Republic, 
Slovakia, Hungary, and Poland have higher Internet penetration than the rest of Eastern 
Europe. Among all post-communist countries, Slovenia and Estonia seem to exhibit 
higher rates of Internet adoption (CDT, 2000; ITU, 1999; World Bank, 2000). These 
variations in Internet penetration among countries with relatively similar socio-economic 
developments in the post-Cold War years suggest that there are multiple factors that 
affect Internet adoption on a country-level basis. The main contribution of this 
dissertation is to identify a group of variables that constitute the most important 
predictors of Internet diffusion in this particular region of the world. 

Research Method 

The dissertation employs aggregate data to determine the multivariate relationships 
between Internet adoption and a number of explanatory variables. 2 These variables fall 
into five categories: economic, political, technological, cultural, and audience factors. 
The study design reflects the theoretical framework discussed in the literature review 
chapter. The dissertation employs t-test, multiple regression, and Tobit analysis to 



The study is limited by data availability. It relies on the latest data published by various international 
organizations. Data paucity accounts, in part, for why Internet adoption in the post-communist countries 
has not been well researched so far. 






17 

determine the statistical significance of a set of explanatory variables. A more detailed 
explanation of the methods is provided in Chapter 4 of the dissertation. 

Dissertation Outline 
After discussing the need for research in Chapter 1, the dissertation continues to a 
more detailed explanation of the significance of the Internet for the political and 
economic growth of society in Chapter 2. Chapter 2 also offers a brief historical overview 
of the major events that led to today's Internet. Next, Chapter 3 reviews relevant 
literature and explains how it is related to the present study. Chapter 3 also describes the 
comprehensive five-dimensional theoretical model proposed and tested in the 
dissertation. Chapter 4 explains the methods used and describes the study design, data 
collection, operational definitions, and scales of measurement. The statistical results are 
analyzed next in Chapter 5. The discussion of the results follows in Chapter 6. Chapter 7 
summarizes the entire dissertation. It also addresses broader implications and limitations 
of the study. Suggestions for future research and theoretical and practical consequences 
also are offered. 



CHAPTER 2 
INTERNET AND SOCIETY 

The Internet's contributions to society are manifold. The Internet is especially 
important for the political and economic development of nations. The first section of this 
chapter presents an overview of Internet development and growth from the 1960s until 
today. A timeline including Internet milestones is provided. After outlining briefly the 
history of the Internet, the chapter describes the importance of the Internet for society as 
it relates to two major issues: the political process and economic development. 

Development of the Internet 

Even though the concepts underlying today's Internet were created in the late 
1960s, the Internet diffusion on a global level occurred only in the 1990s. Several key 
developments that led to the current state of the Internet are presented below. 

The Invention of the Internet 

The Internet was created in the 1960s as a result of close collaboration within the 
American research community (Hafner & Lyon, 1996). Tracing the history of today's 
Internet, it is important to note that four significant aspects of the network were 
considered from the start: technological, management, social, and commercial (Leiner et 
al., 1997). 

The first documented idea of an Internet was discussed in internal memos by J. C. 
R. Licklider at the Massachusetts Institute of Technology (MIT). Licklider described his 
idea of a "galactic network" in these memos in 1962 (Leiner et al., 1997). Other 
developments such as the first work on packet switching theory took place during the 



18 



19 

early 1960s as well (Hafner & Lyon, 1996). The Advanced Research Projects Agency 
(ARPA), a research and development organization funded by the U.S. Department of 
Defense, began operating in 1967. 

ARPA was instrumental in the development of the early Internet. ARPA tested data 
transfer across telephone circuits using packet-switching and thus became the first agency 
to implement a network based on that technology (Leiner et al., 1997). This network was 
named Advanced Research Projects Agency Network-or ARPANET-and became the 
forerunner of the Internet (Hafner & Lyon, 1996). 

Robert Taylor at ARPA found funding to test this so-called "network experiment" 
beginning with a few nodes (Hafner & Lyon, 1996). The first two computers linked on 
the ARPANET were at the Network Measurement Center at the University of California 
at Loss Angeles (UCLA) and the Stanford Research Institute (SRI) at Stanford 
University. The exchange of the first host-to-host message took place on that network in 
1969. Soon after that, two more nodes were added: the University of California at Santa 
Barbara (UCSB) and the University of Utah. Thus, four host computers were connected 
by the end of 1969 into the initial ARPANET (Leiner et al., 1997). These four nodes 
were the first realization of the idea of the "galactic network." Today it is common to 
refer to the 1969 ARPANET as the earliest existing Internet (Hafner & Lyon, 1996). 

From the beginning, the Internet was conceived as a general infrastructure that 
would connect multiple independent networks and support numerous and new 
applications. It is considered an open-architecture network— a network of interconnected, 
independent computers. The structure of the Internet was "foretold" by Paul Baran, an 
engineer at the RAND corporation (Hafner & Lyon, 1996). He created the idea of a 



20 

distributed network, a digital switching technology to connect computers at various 
locations. Baran also came up with the idea to break down the message into small pieces 
(packets) that will travel independently over a network and then reconnect before arriving 
at their final destination. This is still how messages are sent on the Internet. 

A major step toward developing today's Internet was the creation of TCP/IP 
(Transmission Control Protocol/Internet Protocol). This protocol meets the needs of an 
open-architecture network environment. It was compiled in 1972 by Robert Kahn at 
DARPA and Vinton Cerf at Stanford University (Leiner et al., 1997). Basically, the 
TCP/IP protocol allows information bits to travel to destinations independently. The 
Internet Protocol (IP) is responsible for addressing and forwarding of individual packets 
while the Transmission Control Protocol (TCP) is responsible for service features such as 
flow control and recovery from lost packets. 

It is important to note that the researchers at ARPA, its contractors, and several 
universities collaborated closely to create, test, and improve the Internet invention 
(Hafner & Lyon, 1996; Leiner et al., 1997). In the post-Sputnik era, money for research 
from the U.S. government became abundant. As Hafner and Lyon (1996, 23) note, 
science was "the New Frontier." The initial collaboration between the academic research 
community and ARPA, an agency within the Defense Department, led to the successful 
implementation of the ARPANET (Hafner & Lyon, 1996). Therefore, the Internet 
represents a good example of "the benefits of sustained investment and commitment to 
research" (Leiner et al., 1997). A number of people contributed to the development and 
improvement of the initial Internet, but the original research and implementation 
happened mostly under ARPA's funding umbrella in the 1960s (Hafner & Lyon, 1996). 



21 

Below, some milestones in Internet development are shown. 
1962-Primary idea of galactic network 
1967-ARPANET development begins 

1969-First four host computers connected over the ARPANET 
1972-TCP/IP implemented 
1972-First public demonstration of ARPANET 
1972-First email application created at BBN 

1973-First international connections to ARPANET created in England and Norway 
1974-First use of the term "Internet" in a conference paper by Cerf and Kahn 
1978—Trials for a private system, Telset, begin in Finland 
1983— Domain Name System developed at the University of Wisconsin 
1986-NSFNET replaces the ARPANET 
1 989-- World Wide Web invented 

1993— Mosaic, the first Web browser for personal computers is created 
Figure 2-1. Internet timeline. 
Internet Growth and Global Expansion 

The previous section shows that initially the Internet was nothing more than a 
network to used internally within a particular agency. Access to that network was limited 
to a handful of people and information about it was sparse. The first public demonstration 
of the network happened only in 1972. Even at that time, however, people envisioned its 
expansion to a worldwide, borderless network (Rogerson & Thomas, 1998). This was not 
yet technically possible though. The Web browser— the graphical interface of the World 
Wide Web— was created in 1993. The first graphical browser Mosaic was the precursor of 
Netscape and later Internet Explorer. It encouraged faster diffusion of the Internet around 



22 

the world (Winston, 1998). Figure 2-2 shows the growth of Internet hosts over the years 
on a global scale. 



Internet hosts worldwide 



100,000,000 



90,000,000 



80,000,000 



70,000,000 



60,000,000 



50,000,000 



40.000,000 



30,000,000 



20,000,000 



10,000,000 




# a j>j> j> * + j- j> s jp * # # j>j> & $ A A * * *j> # # # * * a * 
# v* jf c^ 5f v* y c^ / v* y <P 4 ^ y/f//(P / v* *W<f* ftfWl # 

Figure 2-2. Total number of Internet hosts. Source: Internet Software Consortium, 2000. 
Clearly, the use of the Internet increased dramatically after the free distribution of 
the Web browser. The World Wide Web was developed by Tim Berners-Lee at CERN in 
Switzerland. The World Wide Web (WWW) uses HTML (hypertext markup language), 
which incorporates text and graphical elements as well as hyperlinks. The introduction of 
the World Wide Web made it easy for non-technical persons to use the computer 
network. As the inventor of the World Wide Web acknowledged, "transferring 
information was too much of a hassle for a non-computer expert" before that (Berners- 
Lee, 1999, 18). Berners-Lee' s goal was not only to make it easier to use the Internet, but 



23 

also to create a system in which different computers with different software could 
connect and "talk" to each other and thus enable researchers to share their work quickly 
and easily. Berners-Lee's first formal proposal for funding was submitted in March 1989 
at CERN, but received no feedback. Another proposal followed with the same result. As 
Berners-Lee wrote (1999, 27), "explaining the vision of the Web to people was 
exceedingly difficult." 

Finally, the World Wide Web was released in 1993. Like the Internet on which it 
runs, the Web has no central location as the online information is distributed: i.e., 
documents are stored on many computers all over the world. The Internet has no main 
node, so it has an infinite storage capacity as a result. 

The global growth of the Internet has been impressive and often labeled 
"revolutionary" and "phenomenal." In fact, it has been argued that the Internet is the 
fastest growing communications technology ever (WIPO, 2001). In 1990, only 22 
countries were connected to the Internet, compared to 226 countries in 1999. The number 
of countries connected to the global network increased tenfold in less than 10 years. As of 
September 2002, there are 605.60 million online users worldwide (Nua, 2002). 

Even though the global expansion of the Internet has been accelerating, disparities 
between regions and countries do exist. The United States and North Western Europe 
have the lion's share of the Internet (ITU, 2000). The Internet is expected to become less 
U.S. -centric though. Forecasts show that Internet growth in the United States will level 
off by 2002, and most continuing growth will be observed in Western Europe and 
developed Asia (Bieler & Stevenson, 1998). 



24 

In the Asia-Pacific region, Australia, Japan, and New Zealand are clearly the 
leaders in Internet adoption. Hong Kong, Singapore, South Korea, Taiwan, and China 
also exemplify fast Internet growth rates (USIC, 2000). The rest of the region and South 
Asian countries in particular lag behind in Internet usage. Japan is expected to continue 
dominating the Asian Internet market (Bieler & Stevenson, 1998). 

In Europe, there are disparities in Internet penetration across East- West and North- 
South lines. Internet penetration rates in Sweden, Norway, Denmark, and Finland 
surpassed 35 percent in 1999 (USIC, 2000). Italy, France, and Spain had relatively lower 
Internet use compared with the Northern European and Scandinavian nations. With the 
exception of Slovenia, the countries of Eastern Europe are further behind. Analysts 
forecast, however, that Eastern Europe and the former Soviet Union are likely to exhibit 
high Internet growth rates (Bieler & Stevenson, 1998; ITU, 1999). 

Projections show that together with Eastern Europe, Latin America will be one of 
the regions to experience substantial increase in Internet usage (USIC, 2000). Internet 
adoption in Latin America and the Caribbean is growing steadily. Brazil, for instance, has 
one of the fastest growing ICT markets in the world (World Bank, 2001). Mexico and 
Argentina, in addition to Brazil, are leaders in Internet usage in the Latin American 
region. 

In Africa, Internet growth rates have been relatively low. According to 2000 data, 
there were 1.5 million Internet users in Africa (USIC, 2000). About 1 million of those~or 
two-thirds of all Internet users, however, were located in South Africa (USIC, 2000). In 
the summer of 2000, more than 300 million people worldwide were using the Internet 
regularly (USIC, 2000). Yet only 0.6 percent of the people living in developing countries 



25 

had access to the Internet (USIC, 2000). As a recent report from the Center for 
Technology and Democracy (2000) notes, Internet disparities bring a danger of dividing 
the countries in the world into "information rich" and "information poor." 

The Internet is constantly evolving and expanding. It is hard to control the Web, 
both in terms of access and content, unlike other media (Perrit, 1999). Regulation has 
been formulated within different countries, including China, Singapore, and Turkey, to 
try to limit freedom of expression and censor the Internet (Cortez, 2000). Legislation to 
restrict Internet in some way (by censoring online information, licensing Internet 
providers, etc.) has been proposed in Australia, Chile, Great Britain, and South Africa 
(Cortez, 2000). In China, for example, cyber cafes must obtain a license from the state 
and ISPs are required to "register their customers with the authorities" (Cortez, 2000). In 
addition, Web sites containing subversive information are blocked. However, it is 
generally hard to implement Internet regulation due to the global and changing nature of 
the network (Perrit, 1999). 

The World Wide Web and the Internet as a whole offer unique opportunities for 
nations, both in terms of political and economic development. These opportunities are 
discussed next. 

Internet and Political Development 

A number of studies have discussed the potential of the Internet to enhance 
democratic governance. Ahmann (1998), for instance, looked at how the Internet can 
increase political participation in South Africa. She found that political information 
online is abundant and that the Internet can easily be used for political education within a 
country (Ahmann, 1998). 



26 

The Internet, then, can strengthen the democratic process by serving as a vehicle 
for political education of citizens. This is achieved not only by keeping the citizens better 
informed (with access to various sources and media online), but also by providing a 
public forum for communication and exchange of information with other like-minded 
people. Thus the Internet can serve as a channel for mediated interpersonal 
communication and community formation. 

Interestingly, in the early 1960s J.C.R. Licklider, one of the key figures in the 
invention of the Internet, conceived it as a network that would, indeed, allow citizens to 
participate in the political process more actively. He envisioned people on the global 
network attending a "giant teleconference" (Hafner & Lyon, 1996, 34). 

Perrit (1999) discusses the role the Internet can play in strengthening both national 
and global governance. He sees the potential of the Internet in four specific areas, all of 
which lead to strengthening international cooperation. Perrit (1999) argues that the 
Internet can strengthen international law and can also empower and improve local non- 
governmental organizations (NGOs). The Internet has the potential to support the 
international security system. Finally, the Internet can strengthen the economic 
interdependence between countries (Perrit, 1999). 

Democracy and the Internet 

Many scholars at different times in history have tried to define what democracy 
means. There is no single clear-cut definition that encompasses all characteristics of 
democracy. The meaning of democracy varies with time and place. The 2000 political 
crisis in Yugoslavia shows the significance of one aspect of a democratic society— free 
and fair elections. When the Serbian people realized that Slobodan Milosevic was trying 



27 

to manipulate election results, they took to the streets because the mandate of their vote 
was not acknowledged. 

In addition to free elections, people in a democracy require freedom of expression. 
This is also supported by a Serbian example, which speaks to the fact that the nature of 
the Internet makes it hard to control. Slobodan Milosevic tried to censor print and 
broadcast media during the 1999 Kosovo crisis. Milosevic's government also tried to 
suppress media freedom by shutting down those media that supported government 
opposition. After being closed down, a popular opposition radio station-Radio B92-used 
the Internet to "broadcast" to the outside world. Thus, the Serbian opposition was able to 
distribute news and information that was censored by other media. This clearly speaks to 
the democratic potential of the Internet. During the Serbian crisis, this new medium 
provided one of the few channels for pro-democratic groups in the country to speak to the 
outside world while other domestic media were strictly censored by the Serbian 
government. As Perrit (1999) notes, "the decentralized nature of the Internet itself . . . 
makes it very difficult for . . . governments to control and censure political thought, 
speech, and action." 

An attempted legislation in the United States also clearly shows the value of a free 
Internet. The proposed Communication Decency Act, which was not accepted by the U.S. 
Congress, shows that freedom of expression is highly valued (Cortez, 2000). The Reno 
vs. ACLU lawsuit exemplifies the value of freedom of expression embedded in the 
American constitution. As Cortez (2000) notes, the following international treaties also 
recognize freedom of expression as a basic human right: the Universal Declaration of 
Human Rights of 1948, the Pact of Civil and Political Rights of 1966, and the Pact of 



28 

Economic, Social, and Cultural Rights of 1966. By extension, all countries that are 
signatories of these international treaties have to harmonize their national legislation with 
the provisions of the treaties (Cortez, 2000). 

The Internet has served as a vehicle for communication for anti-government groups 
worldwide. In China, Lin Hai was given a two-year sentence for sending email addresses 
to an anti-government publication (Cortez, 2000). The Internet provides an outlet both for 
reaching other anti-government activists directly and for publishing relevant information. 
In the case of Malaysia, the Internet allowed the political opposition that was living on a 
remote island to use email to organize anti-government action. 

Diversity of opinions is critical for a well-functioning democracy. The more voices 
are expressed in a public forum, the better. The value of having more information 
available from more sources than before, expressed in a public forum, can hardly be 
disputed— it makes democracy stronger (Held, 1995). 

The Internet certainly makes it easier to get more information from more sources 
than ever before and provides a unique opportunity to communicate online, despite 
geographic distances. Perrit (1999) says: "The ease with which people can participate in 
cyberspace activities enabled the Internet to grow exponentially with virtually no 
governmental oversight. This growth has created a cyber-culture that celebrates freedom 
and distrusts traditional political institutions trying to come to grips with the implications 
of this profound electronic revolution in information technology." 

Poster (2001) discussed Habermas's idea of the public sphere—a "space" where 
citizens deliberate and interact to form public opinion— as it relates to the Internet. 
Jakubowicz says that the "public sphere is a forum of public debate where citizens can 



29 

debate issues of common concern, voice and act on their views and seek to arrive at a 
consensus on matters of general interest" (Jakubowicz, 1998, 12). Gaynor (1996) argues 
that it is difficult to see if the Internet enhances or fragments the public sphere in a 
democracy. Because it offers citizens a venue for expressing public concerns, it does, 
indeed, serve as a part of the public sphere. Thus, the Internet enables individuals to 
influence public policy through the pressure of public opinion. 

Sen (1999a, 10) argues that a democracy requires "the guaranteeing of free 
discussion and uncensored distribution of news and fair comment." The importance of a 
free press and informed citizenry is discussed next. 

Free Press and the Internet 

The Internet affects media organizations around the world. The structure of the 
Internet makes national boundaries irrelevant to the distribution of media messages. The 
Internet also allows people to speak publicly and publish information, just like the 
traditional press in the past. Online media, however, reach wider, global audiences 
(Perrit, 1999). Thus, citizens can access various sources and free media over the Internet. 

Today we seem to take for granted that a free press is a vital part of any democratic 
society (Jakubowicz, 1998). But what specific functions do the media perform in a true 
democracy? According to McChesney (1999), the press has a responsibility to perform a 
public service. A free press, he adds, should encourage diversity of opinions. A 
democratic society presupposes pluralism, i.e. the expression of different opinions. Under 
Communism, people living in the so-called Soviet-bloc countries not only couldn't 
express their opinions freely, but they even felt the pressure to say "the right thing," even 
if it wasn't what they believed (Yakovlev, 1989). The state prevented the existence of a 
free press by enforcing strict censorship and also having financial control of all media. 



30 

The Communist Party also jammed foreign media that broadcast within the Soviet bloc to 
prevent diverse viewpoints from reaching the average citizen. The official press was 
simply a mouthpiece of the communist government. 

In a democratic society, however, the press should not be a mouthpiece of the 
ruling party. On the contrary— it should hold the government accountable for its actions. 
The press is said to have a watchdog function— to hold the government accountable for its 
actions. The media in a democracy should serve the public by ensuring that the 
government is not abusing its powers. A free press in a democratic society is a place 
where deliberation should happen, according to Nader (Nader, 1998). The press, he 
argues, should enliven public debate and engage people into it. Such lively press 
functions as part of the so-called public sphere. The public sphere, as mentioned above, 
enables individuals to give their input through public debate, which is necessary for a 
healthy democracy. If the Internet acts as a space for public debate, then it can empower 
citizens globally. 

Another important basic function of a free press is to keep citizens informed. It has 
been argued that citizens on a global level can stay better informed through the Internet. 
Thus, citizens can participate better in the democratic process in any country (Sen, 
1999b). Sen says that "guaranteeing of open discussion, debate, criticism, and dissent, are 
central to the process of generating informed and considered choices" (Sen, 1999a, 9). 
This is even more important to people in the post-communist countries who, as 
mentioned above, were not allowed to openly state their opinions and engage in free 
discussion prior to 1989. 



31 

Traditionally, the media have served as gatekeepers in society. They filtered the 
information and chose which facts were newsworthy. The media thus used to serve an 
important agenda-setting function: they told the public what issues were important. It has 
been argued that the gatekeeper role and the agenda-setting role of the traditional press 
can be manipulated by governments. The Internet may remove the possibility for such 
direct controls. Internet news are not easy to control, as the gatekeeping and the agenda- 
setting role can be assumed by the average person. 

The Internet transcends national boundaries and withstands governmental controls. 
The Internet also allows people to speak publicly and publish information, just like the 
traditional press in the past by reaching wider, global audiences (Perrit, 1999). Thus, both 
functions are enhanced: (1) citizens can stay better informed (with access to various 
sources and free media over the Internet) and (2) citizens can use the Internet as an 
avenue for communication and debate in a public forum. This suggests the potential of 
the Internet to affect the political process both nationally and internationally, and to shift 
the practice of democracy to more active citizenry. 

In addition to political development, the Internet also affects economic 
development worldwide. The rest of the chapter outlines the most important economic 
impacts at the national level. 

Internet and Economic Development 

There are at least four aspects of the Internet that directly affect developing 
countries. First, the Internet has enabled cheaper production and distribution of goods. 
Second, the emerging Internet economy has played an important role in reforming 
traditional economic structures within countries. Third, the Internet has expanded and 
strengthened global markets by enabling more exports and imports between countries. 



32 

Finally, it has been argued that the Internet has "leapfrogging" potential for less 

developed countries. 

Lower Production and Distribution Costs 

The 20 th century has witnessed a tremendous growth of technology. One of the 
important effects that technology innovations have brought about is the reduced cost of 
production (Christensen, 1997; Sadowsky, 1993; The new economy, 2000). Not 
everybody agrees that reduced production costs or optimized efficiency is generally good 
for society, but still the cost of information exchange today is much lower compared to 
the time when computers first started (The new economy, 2000). This is true for other 
services as well. 

Sadowsky (1993) discusses several ways in which communication technology 
affects economic development in society. He contends that the transportation industry as 
well as the finance sector would be very different without the use of high-speed 
communication and computing technology. Decrease in the price of microelectronics, 
satellite technology, fiber optics, and packet-switching, Sadowsky (1993) argues, have in 
turn accelerated the adoption of modern computing. 

Information technology (IT) has been a major factor in the increased production of 
goods and services by U.S. workers (Barua et al., 1999). IT has also made these workers 
more efficient. Of the 2.6 percent increase in U.S. labor productivity between 1996 and 
1999, more than half was directly related to the information technology sector (USIC, 
2000). 

The Internet intensifies price competition among producers and their suppliers, and 
thus leads to lower prices for consumers as well (DePrince & Ford, 1999; Guthrie & 
Austin, 1996). Guthrie and Austin (1996) contend that product quality is improved as a 



33 

result. DePrince & Ford (1999) argue that labor productivity is increased by Internet 
marketing while searching and transaction costs tend to drop. They predict that "as the I- 
ECON's share of the total economy rises, the magnitude of the Internet's macroeconomic 
impact will also rise" (DePrince & Ford, 1999). 

The Internet Economy 

The economic impact of the emerging Internet economy is also impressive. 
DePrince & Ford (1999) note that the Internet economy has been affecting the following 
areas: hardware, software, intermediaries (such as travel and auction companies), and e- 
commerce. All these sectors have been transformed, directly or indirectly, by the advent 
of the Internet (McKnight & Bailey, 1997). 

DePrince & Ford (1999) note that there is a shift from traditional distribution 
methods to Internet distribution of specific products and services. They distinguish 
between two types of Internet distribution: "Amazonic" and "Dellphic." The former 
refers to ordering products online from a company, such as Amazon.com, that maintains 
a warehouse of its products. Such transactions can be either public or private, individual 
or wholesale. The second distribution method, named after Dell computers, is the 
ordering of a product before it has been manufactured. Users make an order; then the 
producer creates the product or service and ships it to the consumer/buyer. In this case, 
the customer usually responds to promotional efforts by the producer. Both of these 
distribution channels have become popular and are affecting the overall economic 
structure within and between countries. 

The Internet has been seen as a driver of economic boom. The Internet economy 
workforce in the United States grew 36 percent from 1998 to 1999 (Barua et al., 1999). 
According to the U.S. Commerce Department, out of a 5 percent improvement in U.S. 



34 

production of goods and services in 1999, 1.6 percent has been attributed to information 
technology (IT) use (Barua et al., 1999). Labor productivity in the United States 
increased by roughly 3 percent a year from 1995 to 2001; faster growth was observed in 
IT-using industries (Baily, 2001). In other words, studies have shown that IT improves or 
at least contributes to economic productivity. 

The Internet facilitates electronic transactions. Even though electronic commerce is 
not a new phenomenon in itself, the speed of growth has been accelerated by the Internet 
(Atkinson & Court, 1998; Dryden, 1998; WIPO, 2001). Online commerce (e-commerce) 
is expected to comprise 4.4 percent of the U.S. Gross Domestic Product (GDP) by 2002 
(WIPO, 2001). Its growth globally has also been impressive (World Bank, 2001; WIPO, 
2001). 

As Baily (2001) notes, "new technologies are altering the way traditional industries 
operate." The Internet has also strongly affected the following areas: financial services, 
travel agents, stock trading, computer sales, music CDs, software, and book sales. Some 
other types of businesses are likely to be affected by the growing Internet economy as 
well. E-bay is one example where the role of the intermediary is eliminated. Other 
intermediaries such as stock brokers may disappear as well. 

Of course, as DePrince and Ford (1999) point out, not all products and services will 
be affected by the Internet economy. They also note that the Internet has a tremendous 
impact not only on the micro, but also on the macroeconomic level, as it promotes 
improved macroeconomic performance (DePrince & Ford, 1999). 
Global Markets 

The Dellphic and Amazonic distribution channels described above also work in a 
global setting. Business-to-business transactions and international trade are facilitated by 



35 

the Internet. DePrince and Ford (1999) conclude that "the emergence of the Internet 
economy . . . may well rival the introduction of printing, steam power, the telephone, and 
the assembly line as a growth-enhancing innovation." 

Electronic communications facilitate international commerce (Bauer, 1994). The 
Internet introduces new mechanisms for imports, exports, and trade in general (World 
Bank, 2000, 2001). As a global channel, it makes the actual transactions easier and faster. 
One type of product that illustrates well the ease of online transactions is information. It 
is very easy to buy, sell, or transfer data over the Internet. Information transactions 
exhibit economies of scale. The more copies of a data set, for instance, are sold, the 
higher the profit margins. Producing an additional copy of the same data set is virtually at 
no cost. 

Leapfrogging 

The argument exists that developing countries can "leapfrog" as they adopt new 
technologies and thus surpass developed countries. Basically, leapfrogging is the ability 
of countries that are technologically behind suddenly to skip generations of intermediate 
technology and adopt the latest one. This is seen as economically beneficial to countries 
(Singh, 1999). For example, a nation with very low telephone penetration may adopt the 
latest technology for mobile phones and thus jump ahead of other states. Such technology 
is likely to be cheaper, more efficient, and easier to build. Government policy in 
developing countries has been identified as the main barrier to leapfrogging (The new 
economy, 2000). 

The term leapfrogging has been used in the literature in two other ways, in addition 
to skipping an intermediate technological stage. Singh (1999) argues that the term 
"leapfrogging development" has been used to imply that developing countries can skip 



36 

stages of development as a results of telecommunications and thus become members of 
the postindustrial society. The third way in which leapfrogging has been used is to mean 
that telecommunications can itself lead to accelerated development in such countries 
(Singh, 1999). Even though this doesn't happen automatically, the possibility exists at 
least that adopting latest information and communications technologies (ICTs) and 
sophisticated Internet infrastructure will lead to positive economic impact within 
countries. 

Finally, the tremendous impact of the Internet needs to be put into perspective. All 
the possible positive changes will not happen automatically (IMF, 2000b; World Bank, 
2001). Sale (1999) discusses the negative effects of large displacement of human labor by 
the introduction of more sophisticated industrial technologies, such as the Internet. 
Solomon (1998) warns us about the possibility of the Internet becoming an electronic 
mall. Winner (1997) raises questions about the concentration of wealth and power 
"around" new technologies such as the Internet. Another potential drawback of Internet 
use is that the Internet allows dominant ideology transfer and consumer life-styles to 
other countries. The Internet can also widen the gap between developing and developed 
countries. That is one reason for concerns about the uneven Internet diffusion on a global 
level. Despite these potential setbacks, it was argued above that the Internet opens doors 
to improving democratic governance and economic development of nations worldwide. 















CHAPTER 3 
LITERATURE REVIEW 

This chapter first discusses the diffusion of innovations theory. It provides an 
overview of its generalizations and establishes how these generalizations apply to the 
Internet. Several different issues that help in understanding Internet adoption from a 
diffusion of innovations perspective are identified. Next, the chapter reviews the body of 
literature on new media technologies adoption and use. The chapter concludes by 
identifying what major factors affect Internet adoption at the country level. A 
comprehensive five-dimensional analytical framework is proposed. Finally, potential 
interactions between diffusion of innovations and new media technologies research are 
discussed. 

Diffusion of Innovations 

One way to study the Internet is by using traditional diffusion studies. This is not 
an easy task, however. Classification of the Internet as an innovation is difficult because 
of its very complex nature. In addition, the diffusion process at the societal level is not 
fully understood. Rogers (1995, 5) defined the diffusion of an innovation as "the process 
by which an innovation is communicated through certain channels over time among the 
members of a social system." An innovation can be an idea, product, value or a new 
technology. Diffusion of innovations typology is applied below to one of the fastest 
growing new technologies~the Internet. 



37 



38 

Basic Generalizations 

Diffusion of innovations is one of the most popular social science theories and it 

has been used in variety of academic disciplines (e.g., anthropology, sociology, 
communications, marketing, geography, education and health). It started in the early 
1900s with the writings of Gabriel Tarde, a French philosopher, who described many of 
the components of adoption and diffusion in his work The Laws of Imitation (Tarde, 
1903). The first systematic diffusion study, however, was conducted at the University of 
Iowa in 1943. In their seminal work, Ryan and Gross (1943) examined the adoption of 
hybrid seed corn by Iowa farmers. Diffusion research has grown considerably since then. 

Diffusion of innovations offers a linear model for the diffusion process, mostly 
focusing on the individual level of adoption (Rogers, 1995). It posits that innovations are 
typically diffused after going through the following stages: (1) knowledge, (2) 
persuasion, (3) decision, (4) implementation (the actual adoption of the innovation), and 
(5) confirmation stage. The first two stages in this process resemble a communication 
model in which a sender has to get a message across to a receiver. The mass media are 
very important at the knowledge stage (but not as important at the persuasion stage) to 
get the information out that a new product or technology exists to fulfill a particular need 
(Rogers, 1995). ' 

Diffusion of innovations posits that innovations in general follow an S-curve of 
adoption over time. In other words, the first stages of adoption are slower, then we have a 
faster increase in the number of adopters (resulting in a steeper slope of the adoption 



1 At the knowledge level of adoption, we can distinguish three different levels of knowledge: awareness 
knowledge, how-to knowledge, and principles knowledge. 






39 

curve), and finally the curve levels off at the later stages. Empirical data for various 
technology adoptions support the S-curve typology (Severin & Tankard, 1997). 

Technology Innovation Attributes 

Everett Rogers, one of the most influential diffusion scholars (Severin & Tankard, 
1997), identified five attributes that play a role in whether a particular innovation is 
adopted or rejected (Rogers, 1995). They are: (1) relative advantage; (2) compatibility; 
(3) complexity; (4) trialability; and (5) observability of the new idea, product or 
technology. Rogers later added reinvention as the sixth additional attribute. Reinvention 
refers to cases where an existing technology is used for a different purpose than originally 
conceived. For example, if we first thought that the computer was supposed to be used 
for word-processing, we reinvented its use when we started sending e-cards. Rogers 
(1995) contends that 48 to 87 percent of the variability of adoption is explained by the 
perceived attributes of an innovation. 

The first attribute—relative advantage-refers to the question whether the 
innovation is better than what one used before. The perceived relative advantage of the 
Internet, at least at first, will be largely dependent on the marketing and promotion efforts 
put into mass media campaigns. Compatibility is whether the innovation is compatible 
with previous technologies. The next attribute, complexity, refers to the question how 
difficult it is to use the innovation. If it is more complex, people will be less likely to use 
the innovation. Trialability is whether a person can try out the innovation. Finally, 
observability is whether the results of the innovation are readily observable. Clearly there 
are extraneous factors "outside" of the individual that will affect these attributes. 






40 

Interactive Innovations 

Interactive innovations are innovations that depend on the number of people who 
have already adopted a particular innovation (Mahler & Rogers, 1999). In other words, 
the value of the innovation-its relative advantage in the mind of the potential adopter- 
increases if there is a large number of people who are already using that innovation. In 
the case of the Internet, people may be more likely to adopt it if they have the ability to 
be connected to more people who are using the Internet already. A good example to 
clarify this point is email-if your friends have email already, then you can communicate 
with them over the Internet. The perceived value of the technology increases as a result. 

Several technological innovations have been labeled interactive in nature. 
Previous research, however, has not clearly distinguished between interactive and non- 
interactive innovations (Mahler & Rogers, 1999). The classic example of the fax machine 
shows that if only very few people are using the innovation, its value is relatively low. 
Once there is a large number of others that use the fax machine, its diffusion increases 
very fast. 

Mahler and Rogers (1999) examine the diffusion of interactive communication 
innovations (several telecommunications services) and their adoption by German banks. 
They argue that the rate of adoption of such interactive innovations follows a modified S- 
curve (Mahler & Rogers, 1999). The adoption process of interactive innovations such as 
the Internet begins with a slower rate of adoption, but then has a more "pronounced" 
critical mass effect; once the critical mass is reached, the innovation takes off more 
rapidly than projected by the traditional S-curve (Rogers, 1995; Mahler & Rogers, 1999; 



41 

Garrison, 2000). In other words, the adoption of interactive innovations follows a 
modified S-curve. 

The importance of the (perceived) number of other adopters of the interactive 
technology decreases over time. Today fax machines and telephones are so widespread 
(at least in the United States) that potential adopters don't have to consider the interactive 
nature of these communication technologies. As Mahler and Rogers (1999, 720) point out 
about telephone adoption today, "the utility of adopting depends almost entirely on 
factors internal to the individual, rather than on such externalities as the perceived 
proportion of others with whom the individual wishes to communicate by telephone." 
The Internet in the United States has probably reached that point. If we study Internet 
adoption in a developing country, however, network externalities are likely to affect the 
Internet diffusion process (Maherzi, 1997). 

Network externalities are often cited as attributes of information goods and 
services. Basically, network externalities add value to a certain product or service with 
the increase in the number of its users. Shapiro and Varian (1999) discuss in economic 
terms how information goods have higher network externalities and thus are more 
valuable to both the user and the provider. In the case of the Internet— the so-called global 
network of networks, externalities stemming from the number of others who have already 
adopted it should be highly visible. As the Internet becomes more and more prevalent, 
and especially after a critical mass is reached, the significance of the total number of 
users will fade away. 

Network externalities can be direct or indirect. As Mahler and Rogers (1999) note, 
there were indirect network externalities in the case of the VCR diffusion. Two different 



42 

standards were developed—Beta and VHS; tapes dubbed in one of these standards 
couldn't be played on the other. Yet the VCR standard that was adopted was VHS, even 
though it was the technologically-inferior one (this was largely due to better marketing 
efforts on the part of VHS). In the VCR case, critical mass had to be reached for each of 
the two standards (Mahler & Rogers, 1999). This example is applicable to Internet 
adoption, and especially to the adoption of specific Internet software. The diffusion of 
online chat programs such as ICQ will depend largely on the ICQ company's efforts to 
make their program the standard in online chatting. 
Cluster Innovations 

One way to study the Internet using traditional diffusion studies is by positioning 
it as an interactive innovation. Another approach is to examine it as a cluster innovation. 
Prescott and Slyke (1997) argue that the Internet can be best understood as a cluster 
innovation because of its many components. Clearly, classification of the Internet as an 
innovation is hard because of its very complex nature. As Prescott and Slyke (1997) 
suggested, a good way to understand the Internet may be by positioning it as a technology 
cluster innovation— i.e., not as one single technology, but as several technologies working 
with one another. The Internet has many components, many of which are related to or 
dependent on each other. If people adopt email technology, they are more likely to adopt 
Instant Messenger, for example. They will not be able to do this, however, unless they 
have the Internet Explorer browser installed already. 

If we see the Internet as a cluster innovation (several technologies working 
together), we see that it has many relative advantages, depending on which purpose you 
use it for. The Internet is not compatible with anything that existed prior to it, its 
networked structure and way of condensing space and time have been unprecedented. 



43 

The only other technological innovation in history that had such dramatic effect was 
probably the telegraph-"shrinking the world faster and further than ever before" 
(Standage, 1999, vii). 

Both cluster innovations and interactive innovations will have different degrees of 
complexity, trialability and observability. The Internet today may be easier to try out 
compared to five years ago. Any American can go to the public library or to a friend's 
house, for example, and try using the Internet or any of its components and/or services. 
Its results are also readily observable. In general, that should contribute to a faster rate of 
Internet adoption. 

When we talk about the diffusion of an innovation, we basically examine behavior 
change over time. In this case, we are interested in how many people have adopted the 
new technology within a country. Thus we are interested in measuring the number of 
adopters who have reached the implementation stage in the adoption process. It will be 
useful to review next what type of adopter categories exist in diffusion literature. 
Types of Adopters 

There are five basic adopter categories described in diffusion of innovations 
studies on the basis of how fast a member of the social system adopts a new idea, product 
or technology. They are: (1) innovators, (2) early adopters, (3) early majority, (4) late 
majority, and (5) laggards (Rogers, 1995). Diffusion of innovations posits that innovators 
are venturesome, cosmopolite, with higher income and technical knowledge (Rogers, 
1986; Rogers, 1995). The innovators account for 2.5 percent of the total population that 
in the end adopts the innovation. Typically, the early adopters are about 13.5 percent, the 
early majority and the late majority groups are 34 percent of the population each, and the 



44 

laggards account for 16 percent (Rogers, 1995). This reflects the S-curve adoption 
process and follows from the normal distribution of the total number of new technology 
adopters. 

Using Rogers' normal distribution curve of innovators, early adopters, early 
majority, late majority, and laggards, it is easy to predict in which category future 
Internet adopters will fall. This is a useful technique when examining adoption at the 
national level. 
Internet User Profile 

An increasing number of research studies have been conducted in the United 
States to discover the "profile" of Internet users. A number of user studies conducted by 
the Georgia Tech GVU lab in the past years show that most Internet users in the United 
States are male, well educated, and upper income (GVU, 1999). in a study of American 
students Stewart et al. (1998) conclude that, in general, men are more willing to adopt a 
new technology than women. Interestingly, they also suggest that whites are more willing 
to adopt than other cultural groups in the United States. 

Lindstrom (1997) finds similar characteristics for North American Internet users: 
they are mostly male, upper class and well educated. Lindstrom' s survey looks at users in 
the United States and in Canada during two time periods. The second survey shows that 
the typical user has changed and that the user base has broadened (Lindstrom, 1997). The 
typical Internet user in the United States has changed. More women and older people 
have joined the online community. In fact, more recent statistics show that the majority of 
American Internet users are women (Nielsen NetRatings, 2000). This remained true in 
2002 (Nielsen NetRatings, 2002). 



45 

What is the Internet user profile in other countries? Research on Internet use in 
Chile, for example, shows some similarities: Internet users in the city of Santiago 
resemble closely early American users (Mendoza & Alvarez de Toledo, 1997). The 
typical Chilean Internet user can be characterized as young, male, and highly educated. 
The study also reveals that Chilean users tend to have higher income level and to connect 
to the Internet from work or from educational institutions (Mendoza & Alvarez de 
Toledo, 1997). 

One of the few studies on Internet users in Eastern Europe shows a similar 
typology (Dimitrova, 2002). Dimitrova's survey of Bulgarian Internet users finds that the 
majority are highly educated and male (Dimitrova, 2002). Her questionnaire does not 
include income level questions. Similarities in user characteristics are also found in Asian 
countries. The typical Internet user in China, for instance, is also more educated, young, 
and mostly male (Jisi et al., 2001). In addition to the United States, Canada is the only 
other country where the number of female Internet users has surpassed that of male users 
(Nielsen NetRatings, 2002). 

Many studies have tried to explain why some people have adopted the Internet 
while others have not (Goode & Stevens, 2000). Atkin, Jeffres, and Neuendorf (1998) 
examine what differences exist between Internet adopters and non-adopters, and whether 
those individual characteristics can be used to predict the potential users of Internet 
technology. Atkin, Jeffres, and Neuendorf (1998) draw from several theoretical 
frameworks. They see the computer as one of the most discontinuous innovations ever. 
They include media usage variables when looking at what characteristics differentiate 
early Internet adopters from late adopters. 



46 

Atkin, Jeffres, and Neuendorf (1998) examine the demographic characteristics of 
the Internet adopters and non-adopters to test the diffusion of innovations paradigm. As 
suggested in the diffusion typology, they find that Internet users tend to be better-off and 
better educated than the population at large. Atkin, Jeffres, and Neuendorf (1998) also 
find that prior use of and interest in technology is a good predictor of Internet adoption, 
which also fits with Rogers's technologically-knowledgeable innovator profile. Internet 
adopters are also found to be more cosmopolite than non-adopters (Atkin et. al, 1998). 

Many studies support Rogers' generalization that early Internet adopters are better 
educated and upscale (Atkin et. al, 1998; Lin, 1998). Interestingly, several studies suggest 
that individual demographic factors are statistically more significant in predicting Internet 
adoption than attitudinal or communication needs factors (Atkin et. al, 1998). Media 
usage is not an important predictor of computer and Internet adoption (Atkin et. al, 1998; 
GVU, 1998; ITU, 1999; Lin, 1998). Demographic segmentation then can be used by 
diffusion agencies to target specific adopter groups in the diffusion of new technologies, 
especially in the early stages of adoption. Demographic characteristics also should be 
used as predictors when studying Internet adoption by countries with lower Internet 
penetration levels. 
Other Communication Technologies 

Garrison (2000) looks at the diffusion of online research tools in American 
newsrooms. These tools represent a particular aspect of the Internet and its diffusion to a 
particular group of users— American journalists. Garrison (2000) concludes that 
computers are already largely adopted as a newsgathering method and that the "value of 
interactive Internet information-gathering tools" has increased over time. 



47 

The rate of adoption (the number of people who adopt an innovation over a 
specified period of time) varies greatly between different technological innovations. The 
adoption of Nintendo games in the United States, for example, and the VCR was very 
fast (Rogers, 1986). Computers, however, have exhibited a slower rate of adoption. Weir 
(1999) examines the adoption of electronic newspapers and finds that it is different from 
that of other consumer products. Weir (1999) agues that this may be due to the fact that 
electronic newspapers are more like media than like software applications. 

We cannot observe Internet diffusion unless there are personal computers adopted 
in the first place. As Lin (1998) indicates, PC's adoption rate in the United States has 
been relatively slow. She also finds that communication technology ownership is the 
strongest predictor of computer adoption rate (Lin, 1998). Lin argues that demographic 
predictors are still important to forecast computer adoption rates, but that is no longer the 
case for the VCR and cable television. This suggests that communication technologies' 
adoption and diffusion changes over time, both in terms of speed and type of adopter. 
This, in fact, supports Rogers's model, in which it is expected that innovators and early 
adopters may be demographically different than the later adopter groups. Among the 
laggards, early majority, and late majority there are no major demographic distinctions. 
Therefore, the strength of demographic predictors decreases over time. 

Prescott and Slyke (1997) argue that (1) the Internet is a complex technology 
cluster and (2) its adoption is context-specific. The researchers ask the following 
questions regarding Internet adoption at the organizational level: Is the Internet radical 
versus incremental innovation, product versus process innovation, voluntary versus 
involuntary adoption, and pull versus push technology. Internet adoption will have 



48 

different dimensions in different contexts. For example, when a company adopts new 
software that does not change significantly the way business is conducted (just changes 
the application from text-based to windows-based software, for instance), this is an 
example of an incremental change (Prescott & Slyke, 1997). On the other hand, if the 
company introduces a Web-based software that allows customers for the first time to 
place orders online, we have a radical shift from previous organizational practices 
(Prescott & Slyke, 1997). The type of Internet adoption is contextual then-on a case by 
case basis, Internet adoption can fall into one or the other category. 

Levels of Internet Adoption 

Not only is Internet adoption contextual, but it can also be divided into three 

different levels: individual, organizational, and societal. Most research to date has 
focused on either individual or organizational adoption. As Rogers (1995) says, however, 
we can also study adoption at the societal level. 

One of the few studies that applies diffusion of innovations to the societal level 
conducted by Bazar and Boalch (1997) supports the traditional diffusion of innovations 
typology. They argue that Internet diffusion in developing countries is achieved when a 
critical mass is reached and the adoption becomes self-sustaining (Bazar & Boalch, 
1997). The main institutions that play a role in the Internet adoption process within a 
country, according to Bazar and Boalch (1997), are the government, the carriers, funding 
institutions, and the information technology (IT) professional associations. Others have 
also found that national governments affect significantly the Internet diffusion process 
(Lin, 1993). 

There are three different ways of approaching Internet adoption in terms of which 
level of adoption we are looking at. These levels, again, are the individual, 



49 

organizational, and societal level. With a global innovation such as the Internet, however, 
we need more insights about the societal level of adoption. At this level, different factors 
affect the rate of adoption, and diffusion of innovations, while useful, is not sufficient to 
explain country-level technology diffusion. A major limitation of the diffusion of 
innovations typology is the fact that it has been applied mainly to the individual and 
organizational levels. This dissertation contributes to knowledge on diffusion of 
innovations at the societal level. 
Other Considerations for Internet Adoption 

Few studies on Internet adoption make a clear distinction between the software and 
hardware parts of the Internet, a distinction that Rogers (1995) considers important for 
any technology. Such distinction may reveal different patterns of adoption. In addition, 
we need to consider which aspect of the Internet is being examined (e.g., email) when we 
look at the adoption of the Internet from a diffusion of innovations perspective. Such 
delimitations are important yet difficult to make. 

Internet adoption is also subject to whether it is voluntary or not for the individual 
adopter. As Rogers (1995) points out, there are authority or contingent decisions that 
apply to certain adoption situations. The diffusion process is different in cases of optional 
or collective adoption decisions. In this study, we assume that Internet users in the post- 
communist world have made a voluntary decision to use the Internet. 

Wolcott et al. (2001) incorporate national systems of innovation (NSI) literature in 
their discussion of Internet diffusion theory. This body of research underscores the 
importance of national institutions for the diffusion of innovations within countries. NSI 
studies often include Research and Development (R&D) expenditure as a factor affecting 
the successful adoption of the innovation as well as other measures of training and 



50 

education (Nelson, 1993). Typically, countries with higher income levels have higher 
R&D expenditure so including both variables as determinants of Internet usage rates 
could be redundant. 

A better conceptualization of the Internet itself is needed before we can fully 
understand its adoption at either the individual, organizational, or societal level. Some see 
the Internet as a technological innovation. Others describe it as a cluster of technologies. 
Some see the Internet as a culture (Wilson et al., 1996). Yet others talk about the Internet 
as a place (Poster, 1995). Many portray the Internet as a strategic national infrastructure 
(Madon, 2000). Others have discussed its potential to enhance democratic governance 
and improve socio-economic development of developing countries in particular 
(Maherzi, 1997). 

The Internet is very complex in nature and is still evolving. Therefore, further 
research on Internet diffusion and adoption is needed. The diffusion of innovations 
typology is useful overall, but cannot fully explain the adoption at the country level. This 
dissertation also draws on new media technologies literature, which is summarized in the 
next section. 

New Media Technologies Research 

Diffusion of innovations basically postulates that the diffusion process occurs in 
stages over time. A main assumption of that theoretical paradigm is that (1) the diffusion 
process is linear and (2) different groups adopt innovations at different points in time. 
The width of adoption that can describe how widespread a technology is within a country 
is largely based on individual factors. Thus, audience variables are critical for explaining 
levels of Internet adoption. In particular, demographic factors such as personal income, 



51 

and educational level, and attitudinal factors such as cosmopoliteness and innovativeness 
have been identified as important in the diffusion of innovations model. 

According to diffusion of innovations, external factors are also important for 
Internet adoption in developing countries. These include government policies and the 
existence of prior technologies. Multiple technology components are critical in the case 
of the Internet, as the above discussion of interactive and cluster innovations shows. 
Therefore, government policies and political environment as well as technological 
infrastructure can be used as predictors for Internet adoption. 

The nature of interactive communication technologies and the Internet in particular 
requires us to draw from a multidisciplinary theoretical framework to explain variations 
in adoption (Lin, 1998). Several basic sets of factors have emerged in the growing body 
of new media technologies literature as predictors of country level Internet adoption. 
These are economic factors, political climate and policy factors, technology and 
infrastructure, audience characteristics, and cultural factors. This study proposes a 
conceptual framework including the afore mentioned five areas to explain Internet 
adoption at the societal level. 

While certain prerequisites exist that affect adoption of new technologies, it is a 
difficult task to pinpoint exactly what drives Internet diffusion into different countries. 
Most studies to date have shown that the country's economic development plays an 
important role. The next section reviews literature on Internet adoption and economic 
development. 
Economic Factors 

The argument exists that innovations are borne as a result of scientific discoveries. 
Some scholars have argued against this proposition, however, saying that the rate of 



52 

technological progress does not stem directly from basic scientific discovering (Romer, 
1999). Rather, "it is the incentives created by the market that profoundly affect the pace 
and direction of economic progress" (Romer, 1999). Logically, then, technology growth 
is faster when these incentives are stronger. Many scholars have argued that business 
incentives and potential markets determine whether a new technology is introduced or not 
in the first place (Romer, 1999; Sale, 1999). Malecki (2001), for example, contends that 
commercialization has been the main catalyst for the development and growth of Internet 
technology. 

The most evident predictor of Internet penetration in a country is probably the level 
of economic development (Arnum & Conti, 1998; Bazar & Boalch, 1997; Elie, 1998; 
Hargittai, 1999; Wolcott et al., 2001). The country's economic situation has a direct 
effect on Internet adoption. Studies have consistently detected a strong positive 
correlation between the level of economic development and Internet use in a country 
(Arnum & Conti, 1998; Clarke, 2001; Elie, 1998; Gunarante, 2001; Hargittai, 1999; 
Kiiski and Pohjola, 2001). Using World Bank data and classification, Figure 3 shows the 
uneven distribution of Internet hosts across countries with different income levels. 

In addition to empirical data, previous research also shows that countries that are 
better off economically tend to have higher Internet penetration. Bazar and Boalch (1997) 
contend that capital (economic resources) are crucial for adoption of the Internet in 
developing countries. Richer countries in general have more resources to put into the 
service sector of their economies, which includes the information technologies (IT) sector 
(Elie, 1998). Previous research has also supported the assumption that richer countries 



53 



have more telecommunications networks and higher media penetration overall (Maherzi, 
1997). 






Internet Hosts by Income Regions 




Low income 



Lower middle income Upper middle income High income: nonOECD High income: OECD 



Figure 3-1. Internet hosts across income regions. Source: World Bank, 2000. 

Bazar and Boalch (1997) examine Internet diffusion within developing countries' 
context. They define five categories that can be seen as prerequisites for Internet adoption 
in developing countries: national/organizational needs and opportunities in place (this 
concept is related to the nation's vision of the future and relative advantage of the 
country); technology (including infrastructure and Internet technology itself); necessary 
skills and people to introduce the innovation; capital (economic resources); and finally 
good management of the technology adoption and diffusion process. Again, they contend 
that economic resources are critical for Internet adoption at the societal level (Bazar & 
Boalch, 1997). 

One of the few studies on Internet growth in the post-communist countries focuses 
on Internet use in enterprises within the countries (Clarke, 2001). Thus, the unit of 



54 

analysis is the individual enterprise and the dependent variable in the econometric model 
is whether or not an enterprise has access to the Internet. The main question that the study 
addresses is how enterprise ownership and foreign competition affect Internet access in 
the region. The sample includes 2,999 enterprises from Eastern Europe and Central Asia 
and uses the WBES database of the World Bank (Clarke, 2001). The main conclusion of 
the study is that foreign ownership of enterprises positively affects Internet growth. In 
addition, the results suggest that enterprises in smaller countries with higher income 
levels and larger urban populations are more likely to be connected to the Internet 
(Clarke, 2001). 

Most studies of cross-country Internet adoption to date show that national income 
level is an important determinant. Elie (1998) and Hargittai (1999) find a strong 
correlation between Internet penetration in a country and per capita income. Hargittai 
(1999) examines how four country-level indicators affect Internet connectivity among 
OECD members. She includes the economic situation of the country (measured by GDP 
per capita) as well as education level, legal environment (regarding communication 
technologies) as well as infrastructure to explain differences in Internet connectivity. The 
GDP per capita is the strongest predictor in her model: it explains 38 percent of the 
variation. Rodriguez and Wilson (2000) also use GDP per capita as a predictor of ICTs 
use, as measured by and Index of Technological Progress (ITP). They find a strong 
positive correlation between ITP and GNP PPP, also arguing that richer countries make 
more technological progress over time than poorer countries. 

Clearly, GDP or other macroeconomic indicators can be used as predictors of 
Internet adoption in a country. This is also a logical variable since it translates individual 






55 

income, which has been identified as important in the diffusion of innovations paradigm, 
to the country level. 

Kiiski and Pohjola (2001) look at cross-country diffusion of the Internet using the 
Gompertz model of technology diffusion. Thus, their model is longitudinal and their 
dependent variable is the rate of change in the number of Internet hosts from 1995 to 
2000. Their sample includes 23 OECD countries, similarly to Hargittai (1999). Their 
study concludes that the best predictors for adoption are GDP per capita and Internet 
access cost. 

Some researchers, however, find no relationship between national income and 
Internet penetration. Surprisingly, the parameter for income per capita is statistically 
insignificant in a recent study at the World Bank (Dasgupta et al., 2001). Thus, the 
researchers conclude that economic development does not have a strong influence on 
Internet intensity. They extend this conclusion by noting that the disparity in Internet use 
is just a reflection of the "long-standing disparity in telecommunications access" between 
developed and developing countries (Dasgupta et al., 2001, 6). One possible explanation 
for this finding is that income and teledensity (telephone penetration per capita) are 
highly correlated and when one of them is included, the other does not appear to have an 
effect. However, there is no information on the bivariate correlations among the 
predictors given in the study. 

Another economic variable that is clearly related to Internet use is price of Internet 
access. Petrazzini and Guerrero (2000) argue that there is an inverse relationship between 
price of Internet connection and Internet use. They find that once the price of leased lines 
and of tariffs for local calls in Argentina has been reduced, Internet growth increases 



56 

dramatically. Higher Internet prices are generally indicative of more restrictive 
telecommunications policies. They also present a barrier to Internet use of lower income 
demographic groups. However, the price of Internet access in the post-communist 
countries is not available; neither is any other synchronized country-level data on 
governmental policies regarding Internet access. 

In addition to price of Internet connection and basic macroeconomic indicators, the 
size of the service sector of the economy has also been found an important factor for IT 
diffusion (Elie, 1998). The size of the service sector of the economy, however, is 
typically related to national income. Countries with higher per capita income tend to have 
bigger service sectors (World Bank, 2000). 

When comparing some Western European and some Eastern European countries, 
Elie (1998) finds that Internet penetration differs from what would be expected only on 
the basis of macroeconomic indicators, however. Economically highly developed 
countries of Southern and Central Europe (such as France, Germany, Italy, and Spain) 
have lower Internet usage levels than predicted on the basis of their GDP levels (Forrester 
Research, 2000). On the contrary, the Internet seems more "developed" in the 
economically less advanced Eastern European countries of Slovenia, Czech Republic, 
Hungary, Slovakia, and Poland. 

Arnum and Conti (1998) note a similar discrepancy. When calculating Internet 
ratio per country, they show that France and Estonia are very close to each other in their 
Internet penetration. Similarly, Estonia and Slovenia were ranked higher than Hong 



" Instead of using the conventional measures of Internet hosts or Internet users per 10,000 people, Arnum 
and Conti measured Internet penetration per country as what they called an Internet ratio. The Internet ratio 
formula used in their study is: (Internet Hosts + Domains + Web Pages)/ Population. 



57 

Kong, Portugal, and Greece, in terms of their Internet ratio (Arnum & Conti, 1998). The 
study also concludes that several countries with relatively high levels of economic 
activity—for example, Saudi Arabia, Oman, Venezuela, and the U.A.E.— have surprisingly 
low levels of Internet activity (Arnum & Conti, 1998). These results suggest that 
although economic factors are important, there is more at stake in the case of Internet 
adoption at the societal level. 

Political Climate and Policy 

Economic indicators by themselves cannot fully explain the diffusion of interactive 
communication technologies, such as the Internet. Even among rich countries, there is a 
large amount of variation in Internet penetration (ITU, 2000). A study focusing on the 
Organisation for Economic Cooperation and Development (OECD) members concludes 
that GDP by itself explains less than 40 percent of the variation in Internet connectivity 
(Hargittai, 1999). Other studies have indicated the importance of the political stability of 
a country on its overall development and adoption of new technologies (Berg-Schlosser 
& Siegler, 1990). Clearly, political instability will present an obstacle to fast Internet 
diffusion. A good example to support this claim is Rwanda, a country with serious 
political conflict in recent years. The most recent data from the World Development 
Report of the World Bank show that Rwanda has practically zero Internet usage (World 
Bank, 2000). 

The democratization level of the country has been suggested to be an important 
predictor of Internet usage. Studies show some evidence that political freedoms are 
positively related to Internet use (Daly, 2000; Norris, 2001; Rodriguez & Wilson, 2000). 
Rodriguez and Wilson (2000), for instance, argue that a national democratic system is 
critical in the adoption of information technologies such as the Internet. They underscore 



58 

the significance of democratic rights and civil liberties for the creation of a climate where 
information and communication technologies (ICTs) can be easily adopted (Rodriguez & 
Wilson, 2000). Most accounts show a reverse relationship: countries with restricted 
political and civil liberties tend to have lower Internet usage levels, while democratic 
societies tend to encourage Internet growth. 

Rodriguez and Wilson (2000) measure technological progress as a combination of 
TV sets, mobile phones, personal computers, Internet hosts, and fax machines. Their 
study shows the following four factors are critical for national technological progress: a 
climate of democratic freedoms that facilitates the adoption of ICTs; rule of law and security 
of property rights; investment in human capital; and finally low levels of government 
distortions. Interestingly, they found that a transition from the least free stage to a higher 
stage of civil liberties led to an increase in growth rate of technology of 1 8 percentage 
points (Rodriguez & Wilson, 2000). They also claim that "developing countries that 
successfully innovate and diffuse ICTs are able to open their political systems as well as 
their investment and commercial institutions" (Rodriguez & Wilson, 2000, 28). It seems 
that the relationship between Internet use and political freedoms may be circular. 

Another study testing the relationship between democracy and Internet use shows 
that Internet penetration was highly correlated with higher political freedoms and 
democratization levels (Norris, 2001). Norris (2001) also argues that Internet adoption 
within a national context is generally affected by two broad factors: (1) socioeconomic 
development and (2) democratization. Empirical testing showed that national level of 
democratization is highly correlated with a New Media Index (Norris, 2001). When 



59 

controlling for income levels, however, democratization becomes insignificant (Norris, 
2001). 

One way of measuring democratization is by looking at the level of civil liberties 
(Pritchett & Kaufmann, 1998; Rodriguez & Wilson, 2000). Definitions of civil liberties 
vary. One organization that has consistently studied the level of civil liberties around the 
globe is the Freedom House. Their civil liberties ranking focuses on several areas, 
including freedom of expression and belief, independent media, freedom of assembly and 
demonstration, rule of law and human rights, and personal autonomy and economic rights 
in the country. The Freedom House civil liberties ranking emerges as the best proxy for 
democratization and will be used as a predictor in this study. 

In addition to democratization, government effectiveness can also directly impact 
the rate of adoption of new technologies such as the Internet. Previous studies have 
shown that the role of government is crucial in the early stages of Internet adoption 
within a country (Bazar & Boalch, 1997; Lin, 1993). Lin (1993) argues that the American 
government, for example, has played a critical role in the development and growth of the 
Internet in the United States. National government policy has been critical for Internet 
growth in Latin America and Western Europe as well (Petrazzini & Guerrero, 2000; 
Tanner, 1999). 

Policies are directly related to the political climate in a country (Godwin, 1998). A 
study of cross-country Internet diffusion includes policy and urbanization variables in 
addition to income to explain diffusion levels (Dasgupta, et al., 2001). Hargittai (1999) 
finds a strong explanatory power in policies regarding the telecommunication sector in 
her study of Internet connectivity among the OECD countries. Those countries that have 



60 

allowed free competition or even some degree of competition have a higher level of 
Internet penetration than countries with telecom monopolies, other things being equal 
(Hargittai, 1999). 

Kiiski and Pohjola (2001) also include competition in telecommunications 
(measured as existence of some form of competition in telecom markets) as a predictor of 
Internet use. However, competition in their model is not found to be significant, in 
contrast to Hargittai (1999). One possible explanation is that they use both competition 
and access prices in one regression model. That combination can be redundant as the two 
variables are likely to be correlated. Typically there will be an inverse relationship: more 
competition will bring lower prices and vice versa. 

Other researchers have also claimed that it is critical to take into account 
government policies when trying to examine levels of Internet adoption (Petrazzini & 
Guerrero, 2000; Sallai, 2000; Wolcott et al., 2001). This is especially important in the 
post-communist countries, which have been undergoing major political transformation 
have been trying to open up their telecommunications markets only since the early 1990s. 

There is no doubt that government policies impact the pace of new technology 
adoption. Changes in the telecommunications industry in the 1970s and in the 1980s in 
particular have resulted in the introduction of privatization and liberalization policies in 
many countries around the world (Bauer, 1994; Gruber, 2001). Western European nations 
undertook a major shift toward telecom liberalization in the 1980s. Bauer argues that 
three main factors contributed to this shift: innovative equipment resulting from rapid 
technological changes; demand for customized and specialized telecommunications 
services; and desire by the large telecom equipment providers to enter foreign markets. 



61 

It is important to understand the significance of national policy for the creation of 
supportive Internet environment. Specifically, government support is needed to make the 
Internet affordable for the population at large (The Internet's new borders, 2001). A 
competitive telecommunications market becomes critical not only for the development of 
e-business, but for making the Internet more accessible to people. A World Bank report 
(1999) finds that telephone networks expand much faster in those countries that have 
privatized their telecommunications market. Even if the telecom operator is a privatized 
monopoly, it is claimed to be better than a state monopoly operator. Clearly national 
policies in the telecommunications sector are important and need to be considered in the 
study of country-level Internet adoption. 

Government policy is found significant in a study of national computer imports. 
Caselli and Coleman (2001) examine cross-country technology diffusion by looking at 
the determinants of computer imports. Their study suggests that computer adoption is 
related to policy: higher levels of trade openness towards the OECD positively affect the 
number of computers in this case. The results also show that income per worker, 
investment per worker, and secondary education are significant predictors. All of these 
factors are positively related to computer adoption. The study also finds that computer 
adoption is negatively affected when having a large government share in GDP as well as 
large share of agriculture in GDP (Caselli & Coleman, 2001). 

Privatization and liberalization policies directly affect the price of 
telecommunications devices and services. Prices have been claimed to be higher in 
countries with monopolistic telecommunications markets (Horvath, 2002; Jamison, 1995; 
Ryan, 1997). Fish (1998) underscores the importance of privatization and liberalization in 






62 

the post-communist world in explaining, at least in part, the relative differences across 
these countries (Fish, 1998). 

Previous research shows that in general there are several stages in 
telecommunications reform. At the first stage typically we observe privatization of the 
incumbent state-owned telecom operator. At the second stage, competition in the telecom 
market is introduced. General, it has been found that competition is more beneficial to the 
consumer of telecommunications services. 

Fish (1998) examines the power of privatization and liberalization combined as 
determinants of the long-term economic reform in the post-communist countries. These 
two sets of policies are important not only for the success of the economic reform in the 
region, but also for the facilitation of telecommunications development and restructuring 
from a centralized model to an open-market model. Paltridge (2000) argues that 
liberalization in telecommunications facilitates Internet development. Internet access 
prices are important factors as suggested by the vast variability in Internet usage across 
the OECD group (Paltridge, 2000). 

Sometimes privatization needs to be preceded by the adoption of a clear-cut 
regulatory framework in the country (Wheatley, 1999). Wallsten (2002) argues that 
institutional reform and regulations need to be in place before the telecommunications 
firm is privatized in order to have a positive effect. In other words, he recommends 
having regulation first and then subsequent privatization in the telecommunications sector 
in any country. 

It is critical to include the level of privatization when studying Internet penetration 
in the post-communist countries (Estache et al., 2002, Kuentzel et al., 2000; Maddock, 



63 

1997). Privatization has been a difficult process in the transition from centralized state 
economies to market-based economies in those countries (Ellis, 1999; Fish, 1998; 
Gospic, et al., 2000; Gulyas, 1998; Hoelschner, 2000; Jasinski, 1997; Papir & Oleszak, 
2000; UNDP, 1999). As Bauer argues (1994), the post-communist countries "often have 
to create very favorable conditions for infrastructure service providers ... to succeed in 
the attraction of foreign investment capital and technology." Lari (2000) underscores the 
importance of both financial and non-financial aid that international institutions need to 
provide for Eastern European countries. This need is especially acute in the area of 
telecommunications (Lari, 2000). In addition to attracting foreign capital, privatization in 
the telecommunications sector is also very important for competition and Internet growth 
in general (Gulyas, 1998; Sallai, 2000; Sokolov & Goldenstein, 2000). 

As Maddock (1997, 166) points out, "Eastern Europe has followed the Latin 
American model by relying on liberalization to achieve reform but social and political 
disruption has meant that the potential gains have not yet been achieved." In all post- 
communist countries, the main telecommunications operator used to have a strong 
monopoly in the domestic market. In the past, the incumbent telecommunications 
operator often served as a tax collector and regulator (Canning, 1997; Maddock, 1997; 
Michalis & Takla, 1997; Xavier, 2000). The pricing structure was distorted by cross 
subsidies where domestic calls were artificially kept at a lower price, which was 
compensated for most often by high international tariffs. 

Campbell (1995), in his overview of the telecommunications industry in the former 
Soviet Union, makes several observations, which are generally true for the rest of the 
Soviet bloc. He notes that the control of the Post, Telephone, & Telegraph (PTT) is 



64 

completely in the hands of the government. The monopoly, he notes, will be rather 
difficult to break as this sector is considered strategically important (Campbell, 1995). 
The Communist leaders did not view information and communications networks as its 
top priority. Campbell (1995, 25) concludes: "The telecoms sector illustrates the general 
problem that it is not that easy to just start over. The old system left a technological and 
organizational legacy that cannot be overcome quickly." The old structures— both 
institutional and physical— will take time to improve and open up even though the Cold 
War is over. 

The telecommunications sector has being undergoing major reforms since then, but 
it has proven to be quite difficult to modernize. Looking at the former Soviet republics, 
Campbell notes that "telecoms policy is a result of what is happening under the general 
processes of privatization, antimonopoly policy, price regulation, and tax policy as much 
as by specific legislation on telecommunications" (Campbell, 1995, 207). Among the 
former Soviet republics, the Baltic states have been most liberal in giving autonomy to 
the telecom operator. Ukraine and Belarus, on the other hand, show less desire to change 
quickly. The reforms in these countries have been slower (Campbell, 1995). Central and 
Eastern European countries are trying to get closer to the regulatory framework of the 
European Union with their telecom legislation. The process of liberalization of national 
telecom markets is going faster in these five countries: the Czech Republic, Estonia, 
Hungary, Poland, and Slovenia (Bruce, 1999). However, there are different degrees of 
progress not only across the region, but also across these countries. 

Dasgupta et al. (2001) show the importance of policy, measured as the degree of 
private sector competition in a study of cross-country Internet diffusion. However, this 



65 

measure does not directly capture the level of openness in the telecommunications sector 
in particular. Thus, the policy variable in this study can be questionable, even though it 
shows the expected direction of impact on Internet use. In addition, the policy index used 
in the study is based on data from 1995 while the rest of the variables are from 1990. This 
is another shortcoming of the policy predictor. This is a case in point that it is very 
challenging to find a good measure of national policies, especially as they relate to 
Internet development. 

Indeed, it is difficult to measure the recent liberalization and privatization policies 
in the post-communist countries. It can be argued that the overall level of market 
openness within a country is reflected by the level of economic freedom, so the 
Economic Freedom index of the Heritage Foundation could have been used. However, as 
noted about the Dasgupta et al. study (2001), this can be misleading because if a country 
has adopted liberal economic policies overall, its policies in the telecommunications 
sector could still be very restrictive. Thus the level of privatization in telecommunications 
specifically needs to be used, when data permit, to explain Internet adoption levels. 

Privatization by itself, however, may lead to negative results in some cases. 
Wallsten (1999) examined the effects of competition, privatization, and regulation in the 
telecom markets in a number of African and Latin American countries. He found that 
when regulation is introduced with the privatization reform, the effects tend to be 
positive. Again, regulation is also an important aspect of telecommunications reform. 
This study, however, measures only one aspect of telecommunications reform— telecom 
privatization, which is only the first stage in the liberalization process. Future studies 



66 

should try to incorporate not only privatization, but also completion and regulation 
variables, whenever possible. 

Technology /Infrastructure 

Hargittai (1999, 705) notes that "existing telecommunication facilities may be 
crucial for understanding variation in the spread of the Internet." Bazar and Boalch 
(1997) identify technology as one of five determinants Internet adoption in developing 
countries, in addition to economic resources, needs and opportunities, necessary skills 
and people to introduce the innovation, and finally good management of the diffusion 
process. They position technology to include not only Internet technology itself, but the 
general infrastructure within the country (Bazar & Boalch, 1997). 

Arnum and Conti (1998) argue that "[w]hat TV was to the second half of this 
century, what the telephone and the paved road were to the early 20 th century, and what 
the railroad was to the 19 th century, so too is the Internet to the current generation." 
Arnum and Conti (1998) relate the speed of adoption of new technologies to the prior 
existence of other infrastructure. They conclude that the Internet is more popular in 
countries that have long-established infrastructures for communications and 
transportation. Western European countries then will be more likely to have higher 
Internet adoption rates, as they have historically had widespread networks of 
transportation, communications, and other technological infrastructure. 

Daly (1999) emphasizes that infrastructure in general varies widely across regions 
of the world. Since existing infrastructure significantly affects Internet adoption in a 
country (Arnum & Conti, 1998; Bazar & Boalch, 1997; Elie, 1998; Gulyas, 1998; 
Hargittai, 1999; Lin, 1998; Sadowsky, 1993), variations in Internet use are to be 
expected. Gulyas (1998) argues that a modern telecommunications network is a basic 



67 

requirement for a society to become an information society. Elie (1998) contends that the 
existence of a telecommunications network is critical for Internet adoption. Sadowsky 
(1993) discusses the substantial physical and capital investment required to build Internet 
infrastructure before a country can benefit from the Internet. He adds that the 
infrastructure in many developing countries is inadequate for more advanced network 
activities (Sadowsky, 1993). 

In the former Soviet bloc, telephone infrastructure is generally inferior than the one 
in place in Western European countries. Looking at the ex-Soviet republics, Cambell 
(1995) notes that the telephone infrastructure in those countries is outdated and, therefore, 
the quality of service is low. He says: "Old-fashioned and worn-out switching equipment 
meant bad connections" (Cambell, 1995, 25). 

Kiiski and Pohjola's study (2001) shows that infrastructure variables are critical for 
the increase in the number of Internet hosts per capita. Specifically, per capita telephone 
lines and number of PC's are included in their estimation (Kiiski & Pohjola, 2001). The 
researchers note that both telephone lines and PC's are strongly related to GDP so they 
may have a somewhat indirect effect on cross-country Internet diffusion. However, no 
partial correlations are provided. 

One way to measure the existing telecommunications infrastructure in a country is 
telephone penetration (teledensity). Telephone infrastructure has been traditionally used 
in studies on Internet penetration (Clarke, 2001; Elie, 1998; Guillen & Suarez, 2001; 
Hargittai, 1999; Kiiski & Pohjola, 2001). The analytical framework proposed here will 
use telephone density as an infrastructure indicator, but will also include mobile phones 
in addition to residential phones. 



68 

Jupiter Research (2001b) projects a significant global increase in the number of 
mobile Internet users. In the developing world in general, mobile phones seem to play an 
important role. In some African countries, the number of mobile phone subscribers 
surpasses those that use residential phones. Interestingly, Uganda has more mobile phone 
customers than fixed telephone customers as of July 1999 (Minges, 2001). The popularity 
of wireless is evident in the Latin American region as well. Research shows that there is 
an emerging audience in Latin America which will access the Internet only or primarily 
from a mobile phone (Jupiter Research, 2001). The growth of the mobile telephone 
market in Eastern European countries has served as a major incentive for 
demonopolization of telecommunications service overall (Oaca, 2000). 

It is true that Internet usage is dependent on phone line availability and also on 
computer availability. Clearly, to connect to the World Wide Web one needs to have a 
personal computer in the first place. Yet lack of data prevent us from using number of 
computers per capita as an independent variable in this study. Also, even though the 
association between Internet users and number of computers is relatively clear, the 
direction of causality between the two variables is not so obvious. 

Another question that may arise is why not include cable infrastructure to account 
for broadband Internet users. Broadband use in the post-communist countries is very 
limited. Even in Western Europe, broadband Internet has not become very popular 
(Pastore, 2002). Pastore notes that Western European countries are not yet ready to go 
broadband. Specifically, the number of broadband users of all Internet households is 
about eight percent in France, nine in Germany and a very low two percent in Britain 



69 

(Pastore, 2002). In the post-communist countries, it will be quite a while before 
broadband technology becomes available nationwide. 

Audience Characteristics 

Research to date has focused more on the supply side rather than the demand side 
when examining Internet diffusion within countries (Lamberton, 1997). Researchers have 
attempted to answer the question what demographic and attitudinal characteristics affect 
Internet adoption. Diffusion of innovations (reviewed above) shows that innovators and 
early adopters typically have higher socio-economic status, are better educated, more 
cosmopolite, and technologically savvy (Atkin et al., 1998; Rogers, 1995). 

Audience characteristics affect Internet adoption in several ways then. First of all, 
education has emerged as a major determinant of Internet adoption both at the individual 
and country level. The higher the education level of the general population, the more 
likely people are to adopt new media technologies such as the Internet (Caselli & 
Coleman, 2000; Hargittai, 1999; World Bank, 1999). Lack of adequate education, on the 
other hand, can impede Internet diffusion. El-Nawawy (2000), for example, sees 
education as the primary deterrent to Internet growth in the case of Internet adoption in 
Egypt. 

Caselli and Coleman (2000) contend that the choice of technology is driven by the 
human capital in a country. They conclude that countries that have more skilled labor 
adopt technologies that efficiently use that labor, which in turn leads to more capital. In 
contrast, countries with more unskilled labor adopt "less sophisticated" technologies and 
accumulate less capital as a result (Caselli and Coleman, 2000). Press et al. (1998) note 
that there are several determinants of Internet adoption, including the existing 
telecommunication infrastructure, financial resources as well as human capital. The 



70 

characteristics of the human capital then can be seen as drivers of or barriers to Internet 
adoption. 

Kiiski and Pohjola (2001) examine cross-country diffusion of the Internet using the 
Gompertz model of technology diffusion. They include educational level as one of the 
predictors in their study (Kiiski & Pohjola, 2001). Surprisingly, education does not show 
statistical significance. This could be due to lack of variation across the countries, as they 
examine only OECD members. Another possibility is that years of schooling is not the 
best education variable to be used as a predictor of Internet adoption. Both of these 
explanations could be true because when the regression analysis is replicated on a world 
sample of countries and education is measured by university attendance, it becomes 
significant. 

College education is critical in the new communications era. According to the 
World Bank, basic education is important overall, but "new, information-based 
technologies are more demanding in skills for diffusing, interpreting, and applying 
knowledge" (World Bank, 1999, 42). The report additionally notes that "countries at or 
near the technological frontier need strong tertiary education and research institutions to 
compete in the creation of new knowledge" (World Bank, 1999, 42). 

English language proficiency is another important factor for Internet adoption since 
the World Wide Web is still dominated by English-language Web sites (Global Reach, 
2000), even though projections indicate that Chinese will become the dominant Web 
language by 2007. If more users speak English within a country, they are more likely to 
search for the predominantly English-language Web content. Sadowsky (1993), for 



• Even though the domination of the English language is expected to wane, it was still by far the 
predominant online language at the time this study was conducted. 



71 

example, argues that the ability to find online content is critical for Internet popularity in 
developing countries. Interestingly, the rapid growth of Internet use in Bolivia has been 
connected to the increased Spanish-language online content (Minges, 2001). English 
language fluency facilitates not only content retrieval, but also computer and software 
usage. 4 

Wallraff (2000, 61) contends that "most people like new technology better when it 
speaks their own language." Even though the English language has achieved a global 
status, the proportion of English-speaking people is expected to shrink to less than five 
percent in 2050. (Wallraff, 2000). 

Caselli and Coleman (2001) include the fraction of the population who speak 
English as a predictor of computer adoption. They test for the effects of English or 
European language skills of the population. The language variable in this study is defined 
as the proportion of those who speak English as a first language (Caselli & Coleman, 
2001). The results of their study, however, show that fluency in English is not statistically 
significant. 

Hargittai (1999) defines "human capital" as related to Internet usage to include 
educational level and English language proficiency. The addition of human capital 
significantly improves the fit of the regression model. Hargittai (1999) finds that 
education is positively correlated with level of Internet adoption (i.e., the higher the 
educational level in the country, the higher its Internet connectivity). On the other hand, 
lower education levels may prevent mass adoption of the Internet. 



Most computer commands, instructions, and help files are in English. 












72 

Similarly to Hargittai (1999), Kiiski and Pohjola (2001) include English language 
proficiency in their model. Hargittai' s study shows no significance for the English- 
language variable. Even more unexpectedly, the English language variable in the Kiiski 
and Pohjola (2001) analysis has a negative sign. It could be inferred that at this stage of 
adoption among the OECD group of countries, English language is not a significant 
factor. 

The framework proposed here defines audience factors as including both 
educational level and level of English language proficiency. If data permit, both will be 
used as predictors for the level of Internet penetration in the post-communist countries. 
These are the two most important audience characteristics identified in current literature 
on new communication technologies adoption. 

Cultural Factors 

Finally, it has been argued that culture affects Internet adoption in a country (Elie, 
1998; Maitland, 1998). Elie gives the example of European and Asian former Soviet 
republics, claiming that GDP and telecommunications infrastructure cannot explain the 
differences in their Internet positions. Maitland (1998) argues that culture should be 
included as an explanatory variable of the adoption of interactive technological 
innovations, such as the Internet. She argues that adding cultural differences makes the 
understanding of Internet diffusion across countries more robust (Maitland, 1998). 

Researchers acknowledge that the construct of culture is very complex (Jones, 
1997; DiMaggio, 1997; Sondergaard, 1994; Tayeb, 1994). Writing in Britain in the late 
1950s, Raymond Williams conceptualized one of the first definitions of culture. He 
wrote: "Culture is ordinary: that is the first fact. Every human society has its own shape, 
its own purposes, its own meanings. Every human society expresses these, in institutions, 



73 

and in arts and learning" (Gray & McGuigan, 1997, 6). Broadly defined, culture refers to 
the values, religious beliefs, ethics, institutions, customs, and traditions shared by a group 
of people. Thus, cultural traits are innate to all individuals living in a social system. They 
are embedded and transparent within the culture. 

DiMaggio (1997) among others points out that culture is a very complex concept. 
Sociology and psychology researchers have looked at various dimensions of culture and 
cognition. DiMaggio (1997) suggests that culture is fragmented across groups. The move 
from understanding culture as a unified to fragmented phenomenon "makes studying 
culture much more complicated" (DiMaggio, 1997, 265). 

Hofstede (1980), however, sees culture as cohesive and manifested in national 
societies where people within the country share certain beliefs and attitudes. This 
conceptualization supports Williams' definition of culture (Gray & McGuigan, 1997). 
Culture then exists at the societal level as an aggregate of individuals' shared beliefs and 
attitudes. 

Hofstede (1980) developed four dimensions to measure differences across 
cultures: power distance, uncertainty avoidance, individualism and collectivism, and 
masculinity and femininity. The cultural dimension which relates to the adoption of new 
media and the Internet is uncertainty avoidance (Hofstede, 2001). Broadly defined, 
uncertainty avoidance is "the extent to which the members of a culture feel threatened by 
uncertain or unknown situations" (Hofstede, 2001, 161). It follows that countries with 
higher uncertainty avoidance would be more resistant to the adoption of the Internet 
compared with low uncertainty avoidance countries. 



74 

Katchanovski (2000) examines the influence of culture on economic growth in the 
post-communist societies. He concludes that cultural differences do affect growth both 
directly and indirectly. Katchanovski (2000) derives a cultural index on the basis of five 
aspects of culture. His factor analysis identifies one factor consisting of civil society 
index, religion, historical experience, and business index, which is labeled Western 
Culture Index. The addition of cultural variables improves the fit of the regression model, 
with the Western Culture variable showing the highest standardized regression 
coefficient. 

Domanski (2000) looks at religion and its effects on modernization in Eastern 
Europe. The results of the study show a correlation between religiosity and social 
stratification. It is clear that different religions are dominant in the different countries. 
Based on 1993 data from six Eastern European societies, Domanski (2000) argues that 
Poland is the most religious and the Czech republic the least religious of those. The level 
of religiosity across all post-communist nations is expected to vary even more. 

Cultural traits have been given as an explanation for differences in Internet 
adoption among Western European countries (Forrester Research, 2000). Several studies 
support the argument that culture influences the diffusion of technology in a country 
(Maitland, 1998; Rey, 1998). Some studies divide Western European countries depending 
on their geographic location (Beilock & Dimitrova, 2003; Kiiski & Pohjola, 2001). Kiiski 
and Pohjola (2001) include a dummy variable for OECD countries depending on whether 
they are located in southern or northern Europe (N=23). Beilock and Dimitrova (2003) 
regress Internet usage rates against the percent of Roman or Orthodox Catholics in each 
Western European country. Interestingly, these cultural variables show statistical 



75 

significance in both studies. In fact, the model fit improves once the regional dummies 
are added in the Kiiski and Pohjola study (2001). Therefore, they conclude that there are 
certain cultural factors that play an important role in the process of Internet diffusion. 

Drawing from diffusion of innovations, Maitland offers five propositions how 
culture can influence the adoption of interactive technologies at the country level. 
Maitland positions gender equality as a social norm. Interestingly, she argues that the 
diffusion of interactive networks, such as the Internet, will be higher in countries with 
higher gender equality. Maitand (1998) extends the notion of "cosmopoliteness" from 
diffusion of innovations to the country level. Basically, she suggests that openness of 
society can be measured by the country's ethnocentrism, and that cultures low in 
ethnocentrism will begin the diffusion of interactive networks earlier. This argument has 
been supported by others as well (Rey, 1998). The way of measuring countries' 
ethnocentrism can be problematic, however. 

Religion is an important part of culture. In a study of economic reform in the post- 
communist world, Fish (1998) includes religion as a determinant. First he uses three 
categories for religious affiliation, but his ANOVA comparison of means indicates that 
there is, in effect, no statistical difference between the Muslim/Buddhist and Eastern 
Orthodox group. The analysis shows that the number of religion categories can be 
reduced to two, namely the Catholic/Protestant countries as group 1 and the 
Muslim/Buddhist and Eastern Orthodox countries as group 2 (Fish, 1998). Western 
Christianity arguably shows similarity with Western culture/societies. The results in the 
Fish (1998) study, however, show no effect of religion on economic growth. 



76 

The fifth dimension of our Internet diffusion model will be cultural factors. These 
will be represented by dominant religious composition of the population. Incorporating 
this variable with a focus on the three dominant religions also allows for measuring a 
particular aspect of culture- Westernization-though the Western Christianity variable. 

Conceptual Framework 

The diffusion of innovations literature reviewed above posits that diffusion is a 
linear process. It identifies several individual characteristics that are critical for Internet 
diffusion, namely income, educational level, cosmopoliteness and innovativeness traits. 
The income and education levels of the audience are critical in the adoption process. 
Diffusion of innovations research also shows that externalities are important for Internet 
adoption in developing countries. Government policy and preexisting technology are 
among the determinants identified in diffusion of innovations literature. 

New media technologies research, too, supports the notion that the political climate 
and technological infrastructure are critical for Internet use. Past research suggests that 
economic factors remain one of the most significant determinants of Internet adoption at 
the country level. New media technologies studies also show that audience characteristics 
(human capital) as well as cultural factors play an important role in the diffusion process. 

Thus, a multitude of factors emerge as significant for the Internet diffusion process 
at the societal level. The five sets of factors identified in diffusion of innovations and new 
media technologies literature constitute the conceptual framework used in this study. 
They are: 

1 . Economic factors 

2. Political climate and policy factors 

3. Technology/Infrastructure factors 

4. Audience factors 

5. Cultural factors 






77 



This five-dimensional analytical framework for the study of Internet adoption at the 
country level is proposed and tested in this dissertation. Specific variables pertaining to 
each set of factors are described in the following chapter. 

Further Thought 

Rogers posits that innovations have the following five attributes: relative advantage 
(whether the innovation is better than previous ones), compatibility (whether it is 
compatible with previous technologies), observability (how hard it is to use the 
innovation), trialability (whether a person can try out the innovation), complexity (how 
difficult it is to use it) and observability (whether the results of the innovation are readily 
observable). He adds reinvention as the sixth additional attribute. Reinvention refers to 
cases when an innovation is used in a new way/for completely new purposes. 

These six innovation attributes and the five dimensions identified here interplay 
with each other. The framework proposed above includes economic, political climate and 
policy, technology/infrastructure, audience characteristics, and cultural factors. These are 
conceptualized as country-level determinants of Internet adoption. Below, some possible 
influences on Internet adoption patterns as a result of the interactions between the two 
models are described. 

The Internet is a very complex innovation. It can be used for a number of different 
purposes, ranging from email to data searches to online marketing. The relative 
advantage of the Internet will be higher for people who can see its value as a marketing 
tool or even as a way to market their products abroad. In fact, countries in the lower 
middle income range (such as many of the former socialist republics) may gain more by 
using the Internet for import and export of various goods or services. In other words, the 



78 

relative advantage of the Internet will be higher for countries with lower national 
incomes, which also tend to be less involved with international trade. 

The Internet is not compatible with almost anything that existed prior to it. That 
means countries with very low Internet may need to have special training programs for 
potential users to show them how the Internet works. Countries with lower GNP levels 
are less likely to spend money on training courses. The combination of lower income and 
lower compatibility will make the adoption process in those countries for the Internet 
slower, compared with innovations with higher compatibility in a country with the same 
income level. 

The benefits of Internet use are readily observable. Messages travel instantly and 
information (usually) is downloaded very fast. Countries with lower income levels, 
however, do not take advantage of higher speed Internet as much as they lack broadband 
technology (ITU, 1999). Thus, the results of Internet browsing, emailing, downloading 
and so on can be a little slower. If the user does not have a basis for comparison though, 
this interaction is unlikely to have an effect on the rate of Internet adoption. The more 
observable the results are to the users, the more likely they would be to go online again in 
the future. 

The trialability of the Internet as an innovation is also likely to affect its adoption. 
If a person cannot try/does not have access to an Internet terminal, they are unlikely to 
adopt it. Again, poorer nations have lower Internet penetration. Especially in rural/ 
remote areas, people have no access to the Internet. Countries with lower national income 
will have less Internet connections/terminals available for public use, which means that 
the potential adopter will have fewer chances to try out the Internet. This means they will 



79 

be less likely to adopt compared to persons in countries where the Internet is widely 
spread. 

People in more affluent societies tend to have access to more technologies and 
other communication innovations (e.g., personal digital assistants or PDAs). In a way, 
they are more likely to reinvent the Internet from say, just a typewriter or email box, to 
use for online banking. (Online banking, of course, is related to a whole network of 
technologies, software products, bank accounts security and other issues). 

Some governments may adopt policies facilitating Internet penetration, especially if 
they perceive the ability to receive public feedback online as valuable. Thus, the 
relationship here is reversed: those political leaders who see the Internet as a tool for 
encouraging public participation may be more likely to pass legislation that promotes 
Internet adoption. Alternatively, those governments which see the Internet as a threat are 
likely to adopt measures to limit its use. 

As my dissertation proposes, privatization and liberalization policies can facilitate 
Internet development and thus make it more available for people to try. This interaction 
shows that for countries with more favorable market policies, the trialability of the 
Internet will be higher. This interaction is likely to lead to faster adoption rates. Also, if 
the beneficial results of Internet use are obvious for the policy makers, then adoption will 
be promoted by favorable policies. 

People who live in a country with limited communication technologies will see 
more relative advantages of the Internet, as it is likely to be one of the few ways to 
connect with people or get information. People who live in country with extensive 
technological infrastructure will find the Internet more compatible, will have an easier 



80 

time using the Internet and trying it out in general, will find it less complex and may even 
be more likely to reinvent its use because of their previous experience with various 
technologies. 

Those who can read English will be able to find much more information online and 
thus will perceive the Internet as more valuable. A person who can only read Macedonian 
is limited in the amount of online content he/she can locate compared to another person 
fluent in English. If the person uses the Internet for email only, that will not be a major 
issue, however. 

Arguably, people with higher education levels will see more value in Internet 
information, particularly highly specialized information in their professional areas. In 
addition, more educated people will be more likely to want to have professional contacts 
with others around the world. Thus the Internet with its email and bulletin board 
capabilities can be considered even more valuable. 

A person with higher education is more likely to have used a personal computer 
(PC). Therefore, such people will find the Internet more compatible with their previous 
experiences as opposed to those who have never used a PC. 

Some religions may view the Internet as more valuable than others. More closed 
societies definitely are not as likely to embrace the Internet compared with more open 
societies. Also, societies which have been isolated in terms of information from the 
outside world may see the Internet as fulfilling an acute need—they will then perceive the 
Internet as more valuable. 

Some societies are more open to new ideas and find it easier to adopt new 
technologies. Thus members of more open cultures may find it less complex to use the 



81 






Internet and also more compatible with their cultural predispositions. Certain cultural 
traits can affect the way people use and come up with reinvention ideas for various 
technologies. 






CHAPTER 4 
METHODS 

This chapter outlines the methods for testing the five-dimensional analytical 
framework proposed in the previous chapter. The main research question addressed in 
this study is what factors affect Internet adoption in the post-communist countries. Six 
testable propositions are made. The study aims to identify the most significant predictors 
of Internet adoption and explain a significant portion of the variation in Internet use. 
Operational definitions, data collection methods, and hypotheses are proposed below. 

The method chosen is governed by the primary research question and the nature of 
the data. The research design is described next. 

Research Design 

The literature review identified five sets of factors that affect Internet diffusion at 
the country level. These are economic factors, political climate and policy, 
technology/infrastructure, audience characteristics, and cultural factors. To use these 
factors in a systematic, multivariate analysis, they need to be further defined as specific 
variables pertaining to each set of factors. The operationalization of variables is shown in 
Table 4-1. The study uses secondary data from a number of different sources. Data 
sources and collection methods are provided below. The study employs t-test, multiple 
regression, and Tobit analysis to test hypotheses and examine the significance of the set 
of explanatory variables. Theoretical propositions and statistical procedures for testing 
them follow. 



82 



83 



Ta 


3le 4-1. Definition of variables ir 


i the proposed model of Internet diffusion. 




T 


VARIABLE 


DEFINITION 


OPERATIONAL 


NAME 


DATA 


YEAR 


Y 
P 
E 






DEFINITION 




SOURCE 






Internet 


Individuals 


Estimated Internet 


IUR 


ITU 


1999, 




users 


using the 


users per 10,000 






July 


m 

n 




Internet in a 


people 








z 

w 

w 

Q 




particular 












country, per 












capita 












Gross 


GNP per 


The total domestic 


GNP 


World 


2000 1 




national 


capita, 


and foreign value 




Developme 


(1998) 




product 


Purchasing 
Power Parity 


added claimed by 
residents within or 
outside the 
country's borders, 
converted to the 
U.S. dollar value of 




nt 
Indicators 




U 






the goods and 








S 






services which can 








o 

2; 






be purchased within 








O 






the country in the 








y 






local currency. 










Level of 


Length of 


Number of years 


PRIV 


Privatizatio 


1999 


< 
u 


privatization 


privatization of 


since the incumbent 




nLink, 




in 


incumbent 


has been privatized, 




MIGA 




H 


telecommun 


telecom 


either fully or 




(part of the 




J 


ications 


operator 


partially 




World 




Q 

ex 










Bank) 




Democratiz 


Level of civil 


Composite ranking 


DEM 


Freedom 


1999 




ation 


liberties 


based on 14 criteria 




House 






Teledensity 


Number of 
residential 
phones per 
capita 


Number of 

telephone lines per 

1,000 

people plus the 


TEL 


ITU 


2000 
(1998) 


u 






number of mobile 












phones per 1,000 








U-l 






people 









Even though the World Development Indicators Report was issued in March 2000, most of the data are 
from 1999 and 1998. 



84 



Ta 


?le 4-1. Continued. 










T 


VARIABLE 


DEFINITION 


OPERATIONAL 


NAME 


DATA 


YEAR 


Y 
P 

E 






DEFINITION 




SOURCE 




m 


Education 


Enrollment of 


Tertiary percent of 


EDU 


World 


2000 


r ) 


level 


college 


relevant age group, 




Developme 


(1997) 


Z 




students 


gross enrollment 




nt 




U-l 

5 

D 






ratio 




Indicators 
















< 
















Religion 


Predominant 


Two dummy 


MSL, 


Fish; CIA 


1998; 






religion 


variables, using 
Eastern Orthodox 
as a base: 1 if 
Western Christian 


WST 


World 
Factbook; 
U.S. State 
Department 


2000 


_) 






(Catholic or 








< 






Protestant) and 








5 

h 






otherwise; 1 if 












Muslim/Buddhist 








u 






and otherwise. 









Data Collection 

The study uses secondary country-level data from a number of sources. It is 
important to underscore that comparative data for the post-communist countries are 
difficult to find. Fish notes that there "exists no reliable cross-national survey" that 
encompasses all post-communist countries (1998, 37). Aggregate data from international 
organizations—notably the International Telecommunication Union (ITU) and the World 
Bank (WB)-are used in this study. Thus, definitions of indicators are drawn directly 
from the original data source. Alternative data sources are also used to cross-check the 
telecommunications data in particular. 

The main data sources as shown in Table 4-1 are: Freedom in the World published 
by the Freedom House; PrivatizationLink of the World Bank; World Development 
Indicators published by the World Bank; and the World Telecommunication Indicators 



85 

Database published by the ITU. National government sources are also used in cases when 
secondary data are not available from the international organizations noted above. For the 
religion variable, the work of Fish (1998) is used as the main data source and cross 
checked against the CIA World Factbook and country data from the U.S. State 
Department Web site. Additional sources are noted in the statistical analysis. 

Operational Definitions 

It is critical to determine which potential variables best reflect the broad set of 

factors identified in the literature review. 

Economic variable 

The main economic indicator to be used is per capita income. Gross national 
product (GNP) per capita is the variable chosen in this model. This standardized variable 
is a key indicator of national economic development and allows easy comparisons across 
countries (Kennedy, 1998). The GNP measure includes the total domestic and foreign 
value added claimed by residents as well as net receipts of primary income (both 
compensation of employees and property income) from nonresidents (World Bank, 
2000). GNP is calculated in U.S. dollars, using the Atlas method for conversion. 
However, GNP in terms of purchasing power parity (PPP GNP) is more appropriate for 
this study as it reflects the citizen's ability to buy goods and services (Arquette, 2002; 
Beilock & Dimitrova, 2003), such as Internet connection or dial-up services. PPP GNP is 
the U.S. dollar value of the goods and services, which can be purchased within the 
country using personal income in the local currency. Thus, an "international dollar has 
the same purchasing power over GNP as a U.S. dollar has in the United States" (World 
Bank, 2000, 13). The income variable will be log-transformed, based on Rodriguez and 
Wilson (2000). 



86 



A few methodological notes are in order. First, the GNP data for Bosnia and 
Herzegovina and for Ukraine come from the next edition of World Development 
Indicators , using 1999 data-not 1998. Yugoslavia's GNP data is based on data for 
Macedonia, because of the inherent similarity between the two transition economies. 

Political climate and policy variables 

The political climate is measured by the level of democratization in the post- 
communist country, which is demonstrated by the level of civil liberties. Thus, a good 
proxy variable for the level of democratization in the country is the Freedom House 
ranking of the level of civil liberties. This ranking is based on 14 different criteria, which 
include freedom of expression and belief, free and independent media and freedom of 
cultural expression. The Freedom House has collected data on civil liberties within 
countries since 1972. It publishes an annual assessment of the state of freedom within a 
country by assigning a score to each state worldwide. The civil liberties ratings range 
from 1 to 7. A rating of 1 refers to a country considered "Most Free" while a rating of 7 
denotes "Least Free" countries. The scores will be inverted for analysis so that higher 
rankings indicate higher levels of civil liberties in society. The inverted ratings also make 
interpretation of the regression coefficients easier. 

The civil liberties variable is a proxy for the level of democratization in the 
country. In other words, the higher the level of civil liberties, the more democratic the 
country is. Similarly to the Rodriguez and Wilson (2000) study, the civil liberties variable 
is treated as interval even though it is an ordinal variable, similarly to the Rodriguez and 
Wilson (2000) study. The difference between a country with a score of 5 and a country 
with a score of 1 is not exactly 5 times, but this is assumed to be a good approximation 
established by the Freedom House foundation. 






87 

The policy variable used is the length of telecommunications privatization. At first 
glance, privatization can be seen as an economic variable. However, it belongs in the 
political climate and policy category as it is a direct result of the decisions of the political 
leaders in the country. The specific sector of interest in this study is telecommunications. 
There is no consistent cross-country data on overall telecommunications policy in the 
region. Thus, the study uses privatization of the incumbent telecommunications operator 
as the best proxy for telecommunications policy in the country. 

In this study, we assume that earlier privatization of the main telecom operator is an 
indication of successful national policy in the area of telecommunications. In Bulgaria, 
for example, the privatization of the main telecom operator, BTC (Bulgarian 
Telecommunications Company), has been seen as one of the major privatization 
transactions on a national level, comparable with that of the national electricity operator 
and major tobacco companies. The fact that BTC's privatization has been extremely 
difficult and slow is an indication of poor policy making on the part of the Bulgarian 
government. This policy has allowed BTC to continue its monopoly. The reverse is true 
in the case of Hungary: The major telecom operator MATAV was privatized in 1993 and 
is considered a major sign of the success of Hungarian telecommunications 
policymaking. 

The definition of the telecommunications privatization variable is number of years 
since the incumbent telecom operator has been privatized, either fully or partially, at the 
end of 1999. This operationalization provides a method for determining the length of the 
telecommunications privatization in the respective countries. The length of privatization 



BTC's still pending privatization has not been completed as of end of 2002. 



88 



is clearly related to telecom deregulation in these countries, which makes it a good proxy 
variable to be used in this research. 
Technology/Infrastructure variable 

A number of variables that measure Information and Communication Technologies 
(ICT) infrastructure exist. For the purposes of this study, only telephone infrastructure is 
included, even though cable, TV, and other technologies can also play an important role 
in Internet development. Broadband technology is not yet a viable option for Internet 
connection in the post-communist countries. The telephone provides the main mode of 
network connectivity in the region and has been used as a determinant of Internet 
adoption in a number of studies (Beilock and Dimitrova, In press; CDT, 2000; Hargittai, 
1999). 

A logical possibility for an infrastructure variable in this model would have been 
the number of computers per capita. As mentioned in Chapter 3, we cannot account for 
Internet users if we do not have computers in the first place. However, lack of data for the 
majority of the countries of interest prevents us from using that variable. 

The most significant infrastructure variable regarding Internet usage is the number 
of telephones in the country. Thus, the main infrastructure variable used here is the 
number of residential phones per capita. This is measured by the International 
Telecommunication Union (ITU) as telephone mainlines per 1 ,000 people. This measure 
includes all telephone lines connecting a customer's equipment to the public switched 
telephone network (PSTN). 



Another potential measure of political climate and policy would have been the level of economic freedom. 
Economic freedom is commonly measured by the Economic Freedom Index, which is based on 1 areas, 
including government intervention, property rights, and foreign investment. However, economic freedom 
does not directly reflect openness in the telecommunications sector, which is of particular interest in this 
study. 






89 

However, mobile phones play an important role in the technological advancement 
of the post-communist societies. The growth of the mobile market in the post-communist 
countries has been seen as a basic representation of the overall market demonopolization, 
as noted in Chapter 3. Also, technology today allows users to connect to the Internet via a 
mobile phone. The number of mobile phones per 1,000 population is then combined with 
the number of residential phones per 1,000 population into one independent variable 
called teledensity. The only disadvantage of using this composite variable is that we 
cannot compare the separate effects of mainline telephones and wireless. 
Audience variables 

Two critical characteristics of the audience as Internet users were discussed in the 
literature review. The most important variable in this category is educational level. Past 
research shows that early adopters of the Internet tend to be more educated and usually 
hold a college degree (Atkin et al., 1998; Howard et al., 2001; ITU, 1999). Thus, the 
study uses educational level as measured by the gross enrollment ratio in tertiary 
education of relevant age group. This variable shows how many people in the relevant 
age bracket are attending college in a particular country. 

As the literature review showed, people who speak English are more likely to go 
online and have an easier time browsing the Web. The best proxy variable for English 
language proficiency is looking at the number of students who take English as a foreign 
language in school. One way to measure it is as the percentage of pupils in secondary 
education learning English. However, such data are limited only to current or future 
members (the so-called candidate countries) of the European Union. Thus, English as a 
second language is not included in the final regression model, but is recommended for 
future studies. 



90 

Cultural variable 

Arguably, the best cultural measure is the dominant religion in the country. As Fish 
observed about the post-communist region, "in the overwhelming majority of countries, a 
single religious tradition clearly predominates" (1998, 41). National religion is a stable 
variable, not quick to change (Fish, 1998). Using data from Fish (1998) and the CIA 
Factbook (2000), the study incorporates religious affiliation as a cultural variable. 

Orthodox Christianity is the most common religious group in the sample. The other 
two major groups are Western Christians and Muslims. It is argued that these three 
groups are substantially different from each other (Fish, 1998). The following nine post- 
communist countries are predominantly Western Christian (Catholic or Protestant): 
Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, and 
Slovenia. The majority of the countries are predominantly Eastern Orthodox (Armenia, 
Belarus, Bulgaria, Georgia, Macedonia (FYROM), Moldova, Romania, Russia, Ukraine, 
and Yugoslavia (Serbia and Montenegro). Therefore, Eastern Orthodox religion will be 
used as a base group for the dummy variables (coded as 0). The remaining nine countries 
are predominantly Muslim, with the exception of Mongolia. 4 Thus, two dummy variables 
will be employed to represent the three major religions in the region. 
Dependent variable 

The dependent variable of interest in this analysis is Internet users per capita. This 
indicator is measured by the International Telecommunication Union (ITU) as the 
estimated number of Internet users based on the number of Internet hosts in the country. 
The dependent variable in this study will be transformed as a natural logarithm of the 



Mongolia is a predominantly Buddhist country, but is included in the Muslim group in this analysis, based 
on Fish (1998). 



5 



91 

number of Internet users because the distribution of that raw variable is skewed. 
Logarithmic transformations are especially useful in cases when the normality of the 
distribution is violated, usually resulting from the magnitude of the changes in the 
observed variable. Therefore, the interpretation of the beta coefficients is in terms of 
ratios, not in additive terms. When the data have been log-transformed, the slope 
indicates a ratio of change (increase or decrease) in the dependent variable. 

The number of Internet hosts is collected by Network Wizards on a biannual basis. 
These data are used to estimate the number of Internet users per country. Internet hosts 
and Internet users are the most commonly used measures for country-level Internet 
penetration (Press et al., 1998). For complete explanation on data processing of the raw 
Internet host data, check the World Telecommunication Indicators 2000/2001 edition or 
visit the ITU Web site (http://www.itu.int/ITU-D/ict/publications/wti2000-01/). A brief 
explanation follows. 

The number of Internet hosts per capita refers to individual computers connected to 
the Internet—i.e., computers with an actual IP address. Network Wizards collects data on 
active Internet hosts worldwide. This measure of Internet usage has been criticized in the 
past (OECD, 1998a; Minges, 2000; Press, 1997; Zook, 2000). One reason is that Internet 
hosts are misleading if more people use the Internet at Internet cafes rather than at a home 
computer. In addition, Internet host figures are derived on the basis of country code top 
level domains (TLDs) rather than actual physical location of the host. As Minges (2000) 
explains, a host under the .RU country code domain can be located anywhere in the 
world, not necessarily in Russia. By the same token, the so-called generic TLDs (.com, 



A detailed description of Network Wizards' data collection methods can be found at 
http://www.isc.org/dsview.cgi7domainsurvey/new-survey.html. 



92 

.edu, .gov, .int, .mil, .net, and .org) can be located in any country. 6 However, Internet 
hosts are a conservative measure of Internet connectivity (Hargittai, 1999; 
http://www.matrixnetsystems.com). Even though this measure has some shortcomings 
(Minges, 2000; Press, 1997; Zook, 2000), it remains the most common measure of 
Internet usage across countries (Press et al., 1998). 

Another possible dependent variable is the number Internet subscribers per 
telephone line. A study by Dasgupta, et al. (2001) used the number Internet subscribers 
per telephone mainline as the dependent variable measure of national Internet use. Such 
data are collected by the Economist Intelligence Unit (EIU)'s Pyramid Research. 
However, this measure of Internet use can be misleading as many users in developing 
countries log on the Internet from cyber cafes or from work. EIU's data also seem to have 
lower reliability than the Network Wizards data. 

As previous research indicates, there is no absolutely reliable estimate of the 
number of users per host per country, and Internet metrics in general have been 
somewhat inconsistent (Minges, 2000; Press, 1997; Rood, 1999; Zook, 2000). Measuring 
the level of Internet adoption is a challenging task for many reasons. One is that there are 
qualitative differences in terms of usage. For example, some users have access to 
broadband connection or more advanced software than others whoa re limited by a 28K 
or 56K modem. A person using a dial-up modem to connect to the Internet has a different 
experience of the Net than a high-speed user. 

Additional Internet penetration indicators have been developed to include the 
following six different characteristics for country-level Internet adoption: pervasiveness, 



Only seven generic top-level domains exited at the time this study was conducted. More generic TLDs 
have been licensed by ICANN today. 



93 

geographic dispersion within the country, sectoral absorption, connectivity infrastructure, 
organizational infrastructure, and sophistication of Internet use (Press et al., 1998). The 
combination of these six characteristics could provide a fuller picture of the overall 
Internet penetration than using simply one measure, such as Internet users per capita. 
However, collecting data for each of the six variables requires experts to evaluate and 
rank each country for each characteristic. Therefore, such data are unavailable for most 
countries in the world. Another disadvantage of such measures lies in the subjective 
nature of the ranking decision. At the present time, Internet users per capita represents the 
most reliable measure of Internet penetration that can be used. It has the added advantage 
that its values are easily comparable across countries. 

Arguably, the best way to measure Internet users per country is by using panel 
members from a national sample. This involves systematic sampling techniques and is 
more precisely connected to an individual user. In other words, data is collected from 
users' home computers, not from network traffic information. In the United States, there 
are two companies that collect such data: Jupiter Media Metrix and Nielsen NetRatings. 
However, no such research companies exist in the region examined here. 

Even the methods used by such market research companies involve some 
inconsistencies, which makes data comparisons difficult. Minges (2000) argues that 
national surveys of Internet users often look at different demographic groups, for 
example. Many research companies collect data on 16+ and 18+ age group categories. 
Minges (2000) argues that looking at adult populations only disregards an important part 
of current Internet users, precisely the younger population. In addition to age 



94 

breakdowns, geographic breakdowns and category breakdowns (users at work, at home, 
and at school) vary widely across research companies. 

Network Wizards' Internet host data has been commonly used by researchers. 
Companies may adjust their Internet host data using a variety of methods. Matrixnet is 
one company that produces adjusted measures based on Network Wizards' Internet hosts 
counts. Their approach is to adjust the raw data from Network Wizards using statistical 
estimations for localizing the host data. 7 

Several other sources provide their own estimates on Internet users per county. Nua 
is an Irish-based company that reports the number of Internet users per country. They 
collect survey data from national surveys conducted by market research firms or 
journalism reports, and aggregate these data. In some cases, Nua can decide to use one 
source over the other, if the source seems more reliable. The problem is that there is no 
consistency between the methodologies used in different countries. In addition, national 
surveys are often conducted infrequently and inconsistently. The Nua Web site says that 
they consider a user anyone who used the Internet at least once for the past three months, 
or sometimes, for the past six months. That makes the Nua Internet users data unreliable 
and comparisons based on such data very difficult. 

It is very hard to estimate how many people are using the Internet within a country 
(Daly, 1999; Press, 1997; Zook, 2000). One of the challenges for developing countries in 
particular is that people often use email from Internet cafes or cyber cafes. These types of 
use are more prevalent there than they are in the developed countries. The conclusion is 
that ISP subscriptions may underestimate the numbers of Internet users as a result (Daly, 



A detailed description of Matrixnet's methodology can be found at 
http://www.matrixnetsystems.com/company/research/library/how_matrix_gets_its_host_counts.jsp. 



95 

1999). Another obstacle is the unwillingness of ISPs to disclose user data as they are 
facing competing ISPs (Daly, 1999). 

Another company that tracks down the number of Web sites on the Internet is 
Netcraft. However, there is no direct relationship between the number of Web sites and 
the number of Internet users per country (Press, 2000). 

The Model 

The variables described above can be divided into environmental variables (system 
factors) and internal variables (human factors), as shown in Figure 4-1. 



ECONOMIC 



POLITICAL 




INFRASTRUCTURE 



EXTERNAL 



INTERNET 
USE 



VARIABLES 




AUDIENCE 




INTERNAL 



Figure 4-1. Graphic model. 

The environmental variables (system factors) include the economic variable (GNP 
per capita), the political climate and policy variables (democratization and 
telecommunications privatization), and the infrastructure variable (teledensity). The 
internal variables (human factors) include the audience variables (educational level and 
English language proficiency) and the cultural variable (predominant religion). In 
essence, the internal variables are "embedded" in the individual Internet user while the 



96 






external environment variables are conceptualized as factors outside the individual. Thus 
Internet usage is determined by the audience's internal characteristics as well as system 
factors existing externally. 

Statistical Procedures 
Multiple regression is the main method employed in this study. The basic 
assumptions of regression analysis are reviewed briefly below. 

Multiple Regression Technique 

Regression analysis is a type of a multivariate technique that measures relationships 
among a set of variables, and is a popular method in social science research (Kleinbaum 
et al., 1988). It allows researchers to evaluate the relationship between a single, 
continuous dependent variable and one or more independent variables. As Kleinbaum et 
al. (1988) note, there are a five basic assumptions in linear regression: 

1. Y is a random variable, with a probability distribution with a finite mean and 
variance for any fixed value of the independent variable Xj 

In this study, the observations included in the analysis constitute the population of 
interest. Thus, the dependent variable-Internet users per 10,000 population—is not a 
random variable per se. However, it can be argued that it is a random sample where each 
country's probability of being picked equals 1. Therefore, we can treat the dependent 
variable as having a probability distribution with a finite mean and variance for the values 
of the independent variables. 

2. Independence: Y follows a normal distribution, and the Y-values are statistically 
independent from one another 

It is logical to expect that in this model the dependent variable will follow a normal 
distribution after a logarithmic transformation because the gaps between the Ys increase 
as Y increases. It is also expected that the number of Internet users per capita in country 






97 

A will not affect the Internet users per capita in country B, i.e., the values of the 
dependent variable will be statistically independent. Both of these assumptions will be 
tested empirically. 

3. Linearity: Y is a linear function of X (i.e., the mean value of Y is a straight line in 
regard to X) 

This assumption states that the relationship between the dependent variable and the 
independent variables is linear. The assumption is likely to hold true in this model based 
on previous research and also on a brief look at the data. As GNP increases, the number 
of Internet users also increases. Again, the validity of the assumption will be tested both 
visually and statistically in the next chapter. 

4. Homoscedascisity: The variance of Y is the same for any value of X 

Another important assumption in regression analysis is that the dependent variable 
will have the same variance for each value of X. Violations of homoscedascisity— i.e., 
heteroscedascisity, often occur in conjunction with violations in the normal distribution. 
Therefore, it is expected that the dependent variable will exhibit equal variances if it is 
normally distributed. 

5. Distribution: For every fixed value of Xj, Yj is normally distributed 

The final assumption is that the distribution of the Ys will be normal respective to 
the X values. There is no reason to expect violations in the distribution of the residuals. 

In regression, the so-called least squares method "determines the best-fitting 
straight line as that line which minimizes the sum of squares" of the residuals (Kleinbaum 
et al., 1988, 49). Thus, regression analysis allows for estimation of both the direction and 
strength of the relationship between variables. 



98 



The model proposed here is that the dependent variable Y (number of Internet 
users) is a function of ECON, POLI, TECH, AUD, and CUL factors: 

Y= / [ECON, POLI, TECH, AUD, and CUL] 

It is expected that the regression model follows the basic regression formula 
Y=a + P1X1 + p 2 X 2 + . . . + PiXj , where a is the intercept and the (3 is the slope of the 
regression line. The Xs represent the independent variables/predictors. Clearly, when all 
the Xs equal zero, Y=a. When X| increases by 1 (controlling for the other predictors), the 
mean of Y increases by pV When changes in the values of Xj have no effect on the mean 
of Y, then p\=0. It is inferred then that there is no relationship between the dependent and 
independent variables. 

Multiple regression is the best method to answer the question: What are the most 
significant predictors of the variable Y? One of the advantages of multiple regression is 
that it allows researchers to detect the relationship between X and Y while controlling for 
a subset of other predictors. In other words, the partial contribution of the variable X can 
be measured when other covariates are included in the regression model. Regression 
generally looks for a linear association between the variables. It tries to fit the Y values 
on a regression line by minimizing the (squared) distances between the observed value Yj 
and the predicted value of Y. 

The goal in regression analysis is to explain as much of the variation of Y as 
possible. It is tested whether the regression function—using the combination of the Xs— 
will explain more of the variation in Y than only using the mean of Y. The amount of 
variation explained is expressed in an R-square value. The R-square value ranges 
between and 1 . The closer the R-square value is to 1 , the more of the total variation in 



99 

Y is explained by the regression model. Since this analytical model is more 
comprehensive than previous research, it is expected that the R-square will be high. No 
direct comparisons of the R-square value will be possible, however, because no study to 
date has examined Internet use in the same set of countries. 

Stepwise Regression 

Several automatic selection procedures exist that help reduce the number of 
explanatory variables to be used in a regression model. These include backward and 
forward selection methods. Such stepwise regression methods allow researchers to select 
a subset of the initial set of explanatory variables. That technique is especially useful for 
exploratory research (Stevens, 1992). Forward stepwise regression begins by entering the 
predictor that has the strongest partial contribution to the dependent variable Y. At each 
subsequent step, another predictor is entered. The backward stepwise method is the 
opposite: all explanatory variables are initially entered in the model. Variables that lose 
statistical significance after entering the regression model are removed (in contrast to 
forward selection where predictors, once included, cannot be removed from the model). 

Backward regression was chosen as a stepwise method. First, all explanatory 
variables will be entered into the model. Next, the backward selection procedure will 
remove the variable with least partial contribution to the dependent variable Y. Therefore, 
any of the predictors may be removed as insignificant from the final model at subsequent 
steps. The criterion for removal will be F-test probability larger than .10. Also, the 
backward regression procedure is set up so that variables removed earlier do not reenter 
the model at subsequent steps. In this study, we begin by including all predictors in the 
regression model. This complete model is used to test the proposed hypotheses. Next, 
variables will be removed one by one, using backward regression. Thus, a final model in 



100 

which all explanatory variables are statistically significant will be derived. Comparisons 
between the complete model and the reduced/ final model are offered based on R-square 
and Adjusted R-square values. 

Thus, backward regression is used to select the most important explanatory 
variables for the final regression model. This statistical procedure, even though hardly a 
panacea, also reduces the problem of multicollinearity as variables that do not bring 
individual contributions (in other words, do not explain additional variation of Y) are 
removed. However, it is important to note that stepwise regression has some inherent 
issues as a regression type. The use of stepwise regression here is only justified by the 
fact that this is an exploratory study. 

Hypotheses 

Based on the literature review, the following hypotheses are formulated and 
empirically tested, as reported in Chapter 5. 

Proposition 1 

Internet adoption in the post-communist countries will increase over time. Based 

on diffusion trends in other regions of the world-and particularly in Western Europe-we 
expect steady growth in the number of Internet users. 

Hypothesis 1 : The number of Internet users per capita in the post-communist 
countries increased from 1995 to 1999. 

Test: Paired-samples T-test for comparison of means: Yi99 5 < Y1999 
Proposition 2 

Economic factors play a critical role as a driving force of Internet adoption. A 
higher level of economic development will result in higher Internet penetration within the 



101 

post-communist countries. The key indicator of economic development is GNP per 
capita. Thus, 

Hypothesis 2: The higher the level of PPP GNP per capita, the higher the number 
of Internet users per capita. 

Test: H A : p\>0 

Proposition 3 

Restrictive telecommunications policies and lower levels of democratization will 
result in lower Internet diffusion. Conversely, more political freedom and faster 
liberalization in the telecommunications sector will encourage further Internet 
development. 

Hypothesis 3a: The higher the level of civil liberties, the higher the number of 
Internet users per capita. (The civil liberties score will be reversed to show a positive 
association.) 

Test: H A : (3 2 >0 

Hypothesis 3b: The longer the period of telecommunications privatization, the 
higher the number of Internet users per capita. 

Test: H A : (3 3 >0 

Proposition 4 

More developed technology infrastructure will lead to greater levels of Internet 
usage within the post-communist countries. Teledensity is the most critical infrastructure 
determinant of Internet use. Thus, 

Hypothesis 4: The higher the teledensity in the country, the higher the number of 
Internet users per capita. 



102 

Test: H A : p* 4 >0 

Proposition 5 

Audience characteristics, including education level and English language 

proficiency, affect the levels of Internet usage in the post-communist countries. 

Hypothesis 5: The higher the tertiary education ratio, the higher the number of 
Internet users per capita. 

Test: H A : p>0 

Proposition 6 

Cultural predispositions affect the level of Internet use within the post-communist 
countries. Dominant religion, as a major cultural factor, will affect Internet penetration. 

Hypothesis 6: Differences in national religion will affect the number of Internet 
users per capita. 

Test: H A : p\#0; pVO 

Methodological Notes 

Multivariate quantitative studies often face the issue of high correlation between 
the explanatory variables. In such cases, reduction of variables is probably the best 
solution (Agresti & Finley, 1997; Stevens, 1992). Variable reduction is especially 
desirable in this study, where a large number of predictors and a small number of 
observations are examined. 

The population of this study includes the countries of Eastern Europe, the former 
Soviet Union, and Mongolia. The unit of analysis is the country. Statistical analysis will 
allow us to make inferences about the relationships between the dependent variable and a 



Due to lack of language data, the effects of English language proficiency on Internet adoption cannot be 
tested. 



103 

number of independent variables. Parameters will be estimated and hypotheses tested in 
the following chapter. 

The number of observations in this statistical analysis is relatively small (N=28). 
The specific set of countries includes: Albania, Armenia, Azerbaijan, Belarus, Bosnia and 
Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, 
Kyrgyzstan, Latvia, Lithuania, Macedonia (FYROM), Moldova, Mongolia, Poland, 
Romania, the Russian Federation, Slovak Republic, Slovenia, Tajikistan, Turkmenistan, 
Ukraine, Uzbekistan, and Yugoslavia (Serbia and Montenegro). 

The relatively small number of observations is justifiable considering (1) the 
exploratory nature of this study, (2) the strong theoretical base, (3) the 
comprehensiveness of the model, and (4) the fact that it includes the population of 
interest. The independent variables chosen have been identified as significant Internet 
determinants in prior studies of cross-country Internet diffusion. There is very little 
research on Internet adoption in the post-communist countries. Finally, the issues of 
sampling do not apply here as the total number of countries— i.e., the entire population of 
interest— is included in the study. Therefore, the results of this study cannot be criticized 
because the observations include the entire population of interest, namely all the post- 
communist countries. 

The dependent variable in this analysis has an interesting feature: It cannot take on 
negative values. Clearly, the lower end of the variable— number of Internet users— is zero. 
Such variables are called truncated. Some econometric literature suggests that using 
classical linear regression for such dependent variables may produce biased results 
(Gujarati, 1995; Maddala, 1984; Tobin, 1958). In simple terms, the bias comes from the 



104 

fact that the assumption of equal variance of the dependent variable across all values of 
the independent variables is violated when the values of the dependent variable approach 
zero (Maddala, 1984). In such cases an alternative procedure called Tobit is 
recommended. Using linear regression with truncated dependent variables is not a 
problem when only few of the values are close to zero, however. In this study, the lowest 
number of Internet users per 10,000 population is 3.13 for Uzbekistan. Only three 
countries (Uzbekistan, Tajikistan, and Turkmenistan) have less than 5 Internet users per 
10,000 population. 

In a study of worldwide Internet diffusion, Beilock and Dimitrova (2003) found 
that the results of the two methods-Tobit and multiple regression-were, in effect, 
identical. Tobit himself suggests that the results of the regression analysis are a close 
approximation of the results produced by Tobit analysis (Tobin, 1958). In this particular 
study, there is enough variability across the values of Y to assume that multiple 
regression will produce unbiased estimates. The statistical tests for homoscedasticity 
show no serious violation. Still, a Tobit analysis will be conducted as an additional step 
to check the coefficient estimates of the regression against the Tobit estimates. 

Another limitation in this study is the problem of multicollinearity. 
Multicollinearity is evident across the bivariate correlations between the explanatory 
variables (See Table 4-2). As expected, multicollinearity exists in several cases: more 
affluent societies tend to have more developed telecommunications infrastructure (r=.84); 
teledensity is also highly correlated with the level of democratization (r=72) and length 
privatization (r=55). However, it will not be appropriate to eliminate variables identified 
in the literature as significant only because of potential multicollinearity issues. 



105 

The multicollinearity problem is strongest between GNP per capita and teledensity. 
The implication of such multicollinearity is that these two variables, if entered together in 
the model, will affect each other's significance. However, the overall significance of the 
F-test is uncompromized. Further, the stepwise regression method will partially take care 
of the multicollinearity problem because variables that do not independently add to the R- 
square will be removed from the regression equation. 
Table 4-2. Correlation matrix for the continuous indep endent variables. 



Variable 


InGNP 


TEL 


PRrv 


DEM 


TEL 


.84 








PRIV 


.43 


.55 






DEM 


.56 


.72 


.39 




EDU 


.36 


.40 


.23 


.09 



This study measures Internet adoption at a single point in time, except for 
Proposition 1. The five-dimensional framework proposed here is tested by Hypotheses 1 
through 6 examines the relationship between Internet use and a set of explanatory 
variables at one point in time. Thus, this study is cross-sectional, which limits the 
applicability of the findings for future stages of Internet diffusion. 

Another methodological concern is the use of proxies. Data unavailability makes it 
necessary to include proxy variables for a number of factors identified in the literature 
review. In particular, it is very difficult to measure culture empirically. Admittedly, 
religion is only one characteristic of cultural differences. 

The data on Internet users per capita--my dependent variable —need to be 
considered an approximation, an estimate of the actual number of Internet users in the 
country. However, these are the best data on Internet use available at the time of this 



106 

research. Again, these numbers are best estimates and are more useful for comparison 
purposes rather than in absolute terms. The concluding chapter offers more discussion on 
the limitations of this study. 

Finally, it is important to note the following changes in terminology took place 
while this study was being conducted: The country of Yugoslavia has a new official 
name. The new name adopted in March 2002 is Serbia and Montenegro. Also, GNP is 
now called GNI (Gross National Income), according to the World Bank. This new label 
was adopted in 2001. For the purposes of this manuscript, however, the two original 
names are used. 

Next, Chapter 5 describes the results of the statistical analysis and the hypothesis 
testing. 












CHAPTER 5 
RESULTS 

The main goal of this study was to identify the most significant predictors of 
Internet adoption in the post-communist countries. A t-test, multiple regression, and Tobit 
analysis were performed to test the hypotheses proposed in the previous chapter. The 
results showed that by applying the five-dimensional framework, a significant portion of 
the variation in Internet use was explained. Before looking at the hypothesis testing, 
descriptive statistics of this study are presented next. 

Descriptive Analysis 
Internet Users 

The dependent variable used in this study was Internet users per 10,000 population 

(IUR). This indicator is reported by the International Telecommunication Union (ITU) as 
the estimated number of Internet users in the country, per 10,000 population. In this 
study, the variable is log-transformed, using natural logarithm as a base, because the 
distribution of the raw variable is skewed. This logarithmic transformation was 
performed to alleviate the violation of the normality regression assumption. Variations in 
the dependent variable are described below. It is important to note that when data have 
been log-transformed, the regression slope indicates a ratio of change in the dependent 
variable. Therefore, the regression coefficients below reflect a percent change in IUR. 

The range for the transformed dependent variable lnRJR (Log of Internet users per 
10,000) shows a nice dispersion. For easier interpretation, the raw (non-logged) numbers 
are discussed here. Table 5-1 shows that the country with the highest number of Internet 



107 



108 

users per 10,000 people is Estonia (Ranked #1). It has 1,383.5 users per 10,000 
population, followed by the Slovak Republic (#2) with 1,300.7 users, and Slovenia (#3) 
with 1,257.0. These countries are the Internet leaders among the former Soviet bloc 
countries. The Czech Republic (#4), Hungary (#5), and Poland (#6) rank next. At the 
other end of the spectrum, data show Uzbekistan with 3.1 users per 10,000 population (as 
#28 at the bottom), preceded by Tajikistan (#27) with 3.3, and Turkmenistan (#26) with 
4.6. These countries are the laggards in Internet adoption in the post-communist world. 
The other three countries with fewer than 10 users per 10,000 population are Albania 
(#25), Bosnia and Herzegovina (#24), and Belarus (#23). 

There is clearly a wide disparity between the leaders and the laggards in Internet 
adoption among the post-communist countries. The difference between Estonia at the top 
of the ranking and Uzbekistan at the bottom is more than 400 times. Even a country in the 
middle of the ranking such as Romania (#10) has 70 times more Internet users per 10,000 
population than Uzbekistan (#28). Two republics of the former Soviet Union show 
similar discrepancies: Russia (#12) has 19 times the Internet use of Belarus (#23). The 
degree of difference between Kazakhstan and Belarus is smaller but still substantial: 
Kazakhstan has over 4 times the number Internet users of Belarus. 

The mean for the non-logged values of Internet users per 10,000 population is 
290.85 and the median— the middle value-is 80.16, which indicates right-skewed 
distribution. The percentiles show that 25 percent of the data points are under 10.66 and 
25 percent are above 442.65 Internet users per 10,000 people, with Inter Quartile Range 
of 433.99. The logged values range from .50 to 3.14, with a mean of 1.88 and standard 
deviation of .85, as shown in Table 5-2. 









109 



Table 5-1. 


Internet users per 10,000 people in 1999. 


Rank 


Country 


Internet Users 


1 


Estonia 


1383.5 


2 


Slovak Republic 


1300.7 


3 


Slovenia 


1257.0 


4 


Czech Republic 


682.1 


5 


Hungary 


587.7 


6 


Poland 


542.1 


7 


Croatia 


446.7 


8 


Latvia 


430.4 


9 


Lithuania 


278.3 


10 


Romania 


267.8 


11 


Bulgaria 


241.6 


12 


Russian Federation 


183.4 


13 


Macedonia, FYR 


149.2 


14 


Armenia 


85.1 


15 


Yugoslavia, FR 


75.2 


16 


Kazakhstan 


43.0 


17 


Ukraine 


39.5 


18 


Georgia 


36.7 


19 


Moldova 


34.3 


20 


Kyrgyz Republic 


21.4 


21 


Mongolia 


11.1 


22 


Azerbaijan 


10.4 


23 


Belarus 


9.7 


24 


Bosnia and Herzegovina 


9.1 


25 


Albania 


6.5 


26 


Turkmenistan 


4.6 


27 


Tajikistan 


3.3 


28 


Uzbekistan 


3.1 


Source: Y 


ru, 2000. 





Gross National Product 

Gross National Product (GNP) per capita was used as a predictor variable. 
Differences between GNP and GDP are typically relatively small and the two indicators 
are often used interchangeably. GNP is defined by the World Banks' World Development 
Indicators as the total amount of domestic and foreign value added claimed by residents. 
The specific variable used here is GNP in terms of purchasing power parity (PPP GNP), 
the U.S. dollar value of the goods and services that can be purchased within the country 



110 



using individual income in the local currency. In the post-communist countries, GNP 
varies from a low of $1,041 for Tajikistan to a high of $14,400 for Slovenia. Again, the 
relative differences show a considerable gap between top and bottom country in the 
region. Because of its skewed distribution, the GNP is log transformed, using natural 
logarithm as a base. The logged values for this variable-lnGNP-range from 6.95 to 9.57 
(See Table 5-2). 



Table 5-2. 


Descriptive statistics of variables. 








Minimum 


Maximum 


Mean 


St. Deviation 


lnIUR ab 


.50 


3.14 


1.88 


.85 


lnGNP cd 


6.95 


9.57 


8.37 


.65 


DEM 


1 


6 


4.21 


1.55 


PRIV 





9 


1.71 


2.72 


TEL e 


32 


513 


226.79 


148.04 


EDU 


11 


45 


27.72 


10.07 


Religion' 


Eastern Orthodox (EST) 
Western Christian (WST) 
Muslim (MSL) 






Frequency 

10 

9 

9 


a. 11 'K has bee 


n natural- logged. 









See Chapter 4 (Methods) for complete description of variables and data sources. 
This is a GNP PPP per capita measure and it has been natural -logged. 

Due to lack of data, per capita figures for Yugoslavia ( Serbia and Montenegro) are based on Macedonia figures. 
Teledensity combines both residential and mobile phones, 
f. Mongolia was coded as Muslim due to its cultural similarity with this group. 

Democratization 

A proxy for the level of democratization in the country is the Freedom House 
ranking of civil liberties. The civil liberties scores range from 1 to 7. The variable used in 
this study, DEM, was based on a reversed ranking of the Freedom Forum score. In this 
study, a value of 1 means "Not Free" and a value of 7 reflects a "Free" country. 
Variations across this predictor variable range from 1 to 6. In other words, none of the 28 



Ill 

countries in the model is ranked as completely free, according to the Freedom House 
Foundation data. However, nine countries received a ranking of 6, Almost Free. Only 
one country-Turkmenistan-received a ranking of 1, Not Free. Note that civil liberties is, 
in essence, an ordinal variable, but it is treated as interval in this analysis as noted in the 
Methods chapter. 
Telecommunications Privatization 

Telecommunications privatization was measured as the number of years since the 
incumbent telecommunications operator has been privatized, either fully or partially, at 
the end of 1999. There is relatively little variation across this variable. Several of the 
countries of interest have been rather slow in privatizing their major telecom operator. 
Specifically, the telecom privatization variable equals zero in 17 (or 61 percent) of the 
cases. The country that privatized its operator first was Estonia, followed by Hungary and 
Latvia. The majority of countries, however, have not yet privatized or did so very 
recently. This lack of variation may reduce the potential significance of the variable in 
the regression testing. 

Teledensity 

The infrastructure variable was constructed by adding the number of mobile phones 
per 1,000 population to the number of residential phones per 1,000 population. Not 
surprisingly, data vary widely across the population of the study. As shown in Table 5-2, 
the mean teledensity for the 28 countries is 226.79 phones (mobile and residential) per 
1,000 population. The number for Albania is 32 (lowest teledensity in the region) in 
contrast to Estonia where telephone penetration was 513 (highest teledensity in the 
region). 






112 

Education 

Education was measured by the tertiary education ratio, using data from the World 
Development Indicators database. This variable shows how many people from the 
relevant age group attended college. There is relatively little variation across this 
variable: the mean for all countries was 27.72, with standard deviation of 10.07 (See 
Table 5-2). The top country with a ratio of 45 is Estonia whereas Albania is at the bottom 
of the list with a ratio of 1 1 . 

Religion 

Predominant religion was used as a cultural determinant. The frequencies show that 

nine of the post-communist countries are predominantly Western Christian (Catholic or 
Protestant), ten countries are predominantly Eastern Orthodox Christian, and nine are 
predominantly Muslim/Buddhist. Mongolia is a predominantly Buddhist country, but it 
was coded as Muslim, based on Fish (1998), due to its cultural similarity to this group. 
To test the proposed hypothesis, two dummy variables were constructed. Western 
Christian (Catholic or Protestant) was coded as 1 and otherwise in the first dummy 
variable. In the second dummy variable, a country was coded as 1 if predominantly 
Muslim or Buddhist and otherwise. Thus, Eastern Orthodox Christianity was the 
category for religion. 
Bivariate Correlations 

Next, bivariate correlations between the dependent variable-log of Internet users 
per 10,000 population-and the set of independent variable, excluding the religion 
dummies, were run. The Pearson correlations are shown in Table 5-3. The variable with 
the highest bivariate correlation coefficient is teledensity, followed by democratization. 
Both of these variables are highly correlated with InlUR and both coefficients are well 



113 

above .80 whereas the correlation with InGNP is close to .80. The bivariate correlation 
coefficients are lower for telecommunications privatization (r=.50) and education (r=25). 
All coefficients are positive, indicating a positive relationship between IUR and the 
predictors, without controlling for the other variables. 
Table 5-3. Pearson correlations between dependent and independent variables. 



Variable 


InlUR 


InGNP 


.795 


DEM 


.843 


priv 


.503 


TEL 


.895 


EDU 


.245 



After initial examination of the bivariate correlations, a multivariate regression was 
conducted. The results of the multiple regression analysis are presented in the next 
section. 

Regression Results 
Statistical Assumptions 

The classical assumptions of linear regression are: first, a linear relationship 

between the dependent variable and the determinants; second, homoscedasticity (or equal 
variance of Y for all Xs); and third, normal distribution of the dependent variable across 
the values of the independent variables. All of these assumptions are satisfied in this 
analysis, either initially or after subsequent transformation of variables. An examination 
of the histograms as well as statistical tests showed no significant deviations. 






114 






0) 



o 
O 

— 

'c 

3 

E 

£ 

o 

o 
■ 

(0 

o 

Q. 

0) 



w 

0) 
(0 

3 
c 

L. 

V 

*-> 

c 




eiuojsg 

liqnday >|eaois 
BjuaAois 
aiqnday qoazo 
AjB6uny 
pueiod 

BjAjen 

BiuBnqin 

BIUELUOH 

Bue6|ng 
BJapaj uBissny 

yAd 'BjUOpeOBlAJ 

Bjuaixuv 

yd 'E!AB|So6riA 

UBJSq>)BZB^ 

aiuBj^n 

b|6jo89 

baopioim 

liqnday zA6jA» 

bi|o6uoiai 

uBhBqjazv 

snjBieg 

jay pub Bjusog 

BjUBqiv 

UBJSIU8UJ)(jni 

uBjs^aqzn 



sjasn iaujami 



C/2 



c 

8 

o 

(*> 

O 

i_ 

o 

c3 



c/3 

3 

I 

4—* 

c 



•a 

2 



in 



115 

Hypotheses Testing 

The five-dimensional theoretical framework proposed the following function: Y= / 
[ECON, POLI, TECH, AUD, and CUL], where the dependent variable Y is the number 
of Internet users per capita. Y was defined as a function of economic, political climate 
and policy, technology, audience, and cultural factors. The specific hypotheses stated on 
the basis of this function are tested below. 

Hypothesis 1 predicted that the average number of Internet users in the region 
increased significantly from 1995 to 1999. A paired-samples t-test for comparison of 
means was performed. Due to lack of data for 1995, only 19 countries are included. The 
results of the t-test comparing Internet use in 1995 and 1999 are significant (t ( i8> =3.07; 
p=.007). The t-statistic is positive, which indicates that the number of Internet users 
increased significantly from 1995 to 1999. Thus, hypothesis one is supported. 

Despite the general increase in the number of Internet users in 1999, variations 
across the post-communist countries remained stark. Figure 5-1 shows the unequal 
distribution of Internet users per capita based on 1999 data. It is obvious that the degree 
of difference in Internet use between the countries in the region is substantial, with three 
countries clearly at the top: Estonia, the Slovak Republic, and Slovenia. 

The second hypothesis predicted that higher per capita income would contribute to 
higher number of Internet users per capita. Table 5-4, Model 1 shows the results of the 
multiple regression analysis. The results indicate that this hypothesis is supported. The 
standardized beta coefficient for GNP (B=.23, p=.031) is positive and statistically 
significant at the .05 level (See Table 5-4, Model 1). As expected, higher InGNP leads to 
higher lnlUR. 






116 



The third hypothesis predicted a positive relationship between the political and 
policy variables and Internet adoption levels. Hypothesis 3a stated that the higher the 
level of civil liberties in the country, the higher the number of Internet users per capita. 
The results of the regression analysis show support for this relationship. As Table 5-4, 
Model 1 illustrates, the standardized beta coefficient for DEM is positive and statistically 
significant (B=36, p=.001). Thus, the regression results show strong support for this 
hypothesis. 



Table 5-4. Regression results for Internet users. 3 ' b ' c ' 


i 




Variable 


Model 1 


Model 2 


Model 3 


Model 4 


InGNP 


.23* 
[.031] 


.23* 
[.027] 


.22* 
[.033] 


.22* 
[.036] 


DEM 


.36*** 
[.001] 


.36*** 
[.000] 


.36*** 
[.000] 


41*** 
[.000] 


PRIV 


.09 
[.141] 


.09 
[.135] 


— 


— 


TEL 


.24 
[.148] 


.25 
[.074] 


.32* 
[.025] 


.28* 
[.039] 


EDU 


-.10 
[.115] 


-.10 
[.099] 


-.10 
[.115] 


— 


WST 


.01 
[.471] 


— 


— 


— 


MSL 


-.26* 
[.016] 


-.26** 
[.008] 


-.28* 
[.014] 


-.20* 
[-024] 


R-Square 


.923 


.923 


.918 


.912 


Adjusted R- 
Square 


.896 


.901 


.899 


.897 


F-test 
[Sign.] 


34.107 
[.000] 


41.768 
[.000] 


49.226 
[.000] 


59.773 
[.000] 



~a. Dependent variable: Log ot Inlemet users. 

b. The table reports Standardized Beta Coefficients with significance in brackets. 

c. The coefficients are based on one-tailed tests. 

d. *p<.05;*»p<.01;*»*p<001. 

Hypothesis 3b proposed that the longer the period of telecommunications 
privatization, the higher the number of Internet users per capita. Even though the 
coefficient for PRIV is positive, this variable is not statistically significant (B=.09, 






117 

p=.141). Thus, we cannot reject the Null hypothesis that p ? is larger than 0. Hypothesis 
3b is not supported, as shown in Table 5-4, Model 1 . 

Hypothesis 4 predicted that higher teledensity in the country will lead to higher 
Internet use per capita. Even though the sign of the beta coefficient is positive (B=.24, 
p=.148), the partial t-test in the regression table is not significant at the 95 percent 
confidence level (Table 5-4, Model 1). Thus, the results of the regression analysis show 
no support for Hypothesis 4. 

The results of the regression analysis do not support Hypothesis 5 either. Higher 
tertiary education ratio seems to be related to lower number of Internet users per capita 
(B=-.10, p=. 1 15). However, the results for this predictor are not significant (See Table 5- 
4, Model 1). Thus, we cannot reject the Null hypothesis and conclude that educational 
level is not positively related to Internet use. 

The last hypothesis predicted that differences in the predominant national religion 
would affect the number of Internet users per capita. The results of the regression 
analysis show partial support for Hypothesis 6, as reported in Table 5-4, Model 1. The 
standardized beta coefficient for the Muslim dummy variable is negative and statistically 
significant (B=-.26, p=.016) whereas the coefficient for the Western Christian group is 
not significant (B=.01, p=.47). Thus we conclude that Muslim religion is significantly 
and inversely related to Internet use, but Western Christianity is not, when examining the 
post-communist countries. Bear in mind that the values of the religion dummies are 
relative to Eastern Orthodox as the base and do not represent absolute values. 

The most surprising result is that teledensity is not statistically significant in the 
complete regression model. This could be partially due to the high correlation between 



118 



TEL and GNP. Another possible explanation for the lack of significance of TEL is that 
the countries in the region are relatively close in terms of existing infrastructure, 
compared with countries in sub-Saharan Africa and Western Europe, for example. 
Teledensity may not be a constraint for Internet use in the post-communist countries. 
Further analysis was performed to determine which explanation is more plausible. Only 
income, democratization, and Muslim religion are statistically significant in the full 
regression model. 
Table 5-5. Summary of hypothesis testing. 



Hypothesis 


Statement 


Result 


HI 


The percent of Internet users in the post- 
communist countries increased from 1995 to 
1999. 


Supported 


H2 


The higher the level of PPP GNP per capita, the 
higher the number of Internet users per capita. 


Supported 


H3a 


The higher the level of civil liberties, the higher 
the number of Internet users per capita. 


Supported 


H3b 


The longer the period of telecommunications 
privatization, the higher the number of Internet 
users per capita. 


Not Supported 


H4 


The higher the teledensity in the country, the 
higher the number of Internet users per capita. 


Not Supported 


H5 


The higher the tertiary education ratio, the 
higher the number of Internet users per capita. 


Not Supported 


H6 


Differences in national religion will affect the 
number of Internet users per capita. 


Partially 
supported 



The results of the multiple regression analysis show that the six predictors 
combined explain 92 percent of the variation in Internet use (See Table 5-4, Model 1). 
The adjusted R-square is .896, which is also substantial. Three of the predictors in the 
complete model-GNP, DEM, and MSL—are significant at the .05 probability level. 
However, the other four predictors— PRIV, TEL, EDU, and WST-- are not statistically 
significant. Therefore, subsequent regression models are developed to determine the most 
robust model for these data. 



119 

Final Model 

Backward regression was employed to determine the best regression model, the one 
employing the fewest predictors and having the strongest explanatory power. Adjusted R- 
square values were used as a criterion, with higher Adjusted R-square indicating a better 
model. At each step, the backward regression procedure removed the variable with the 
least contribution from the complete model. The criterion used to delete variables was F- 
test probability higher than .10. Using this procedure, the first variable to leave the model 
was Western Christian religion (See Table 5-4, Model 2). At the next step the 
telecommunications privatization variable was removed (Table 5-4, Model 3). Next, 
tertiary education ratio was removed (Table 5-4, Model 4), with income, democratization, 
teledensity, and Muslim religion remaining in the model. Of the seven initial variables, 
only four were left in the reduced final model. 

The four predictors that remained in the final regression model were income 
(InGNP), democratization (DEM), teledensity (TEL), and Muslim religion (MSL). These 
four predictors, collectively, explain 91 percent of the variation in Internet use. The 
adjusted R-square for the final model is .897, which is slightly higher than the value in 
the complete model. It is also evident that the F-test from Model 1 (complete model) to 
Model 4 (final model) increased. The ANOVA tables for the each of the four regression 
models are presented below with a comparative discussion. 

The complete model of the multiple regression is shown in Table 5-6. Based on this 
model, the following regression equation line is derived: 

InlUR = -1.458 + .3021nGNP + .195DEM + . 027PRIV + .001TEL - .008EDU + 
.020WST - .462MSL + e 






120 



Table 5-6. ANOVA Table foi 


Complete Model.*' b,c 






Model 1 


Beta 


Std. Error 


Std. Beta 


t 


Sig. 


Constant 


-1.458 


1.219 




-1.196 


.246 


InGNP 


.302 


.152 


.231 


1.983 


.061 


DEM 


.195 


.056 


.357 


3.502 


.002 


PRIV 


.027 


.024 


.086 


1.109 


.281 


TEL 


.001 


.001 


.235 


1.070 


.297 


EDU de 


-.008 


.006 


-.096 


-1.241 


.229 


WST 1 


.020 


.260 


.011 


.075 


.941 


MSL 


-.462 


.201 


-.259 


-2.302 


.032 



a. Dependent Variable: Log Internet users 

b. N=28 

c. Two-tailed 

d. Because education was not showing a significant effect, and we suspected some indirect relationship 
with GNP for that, the GNP variable was removed from the model and all coefficients were 
recalculated. However, neither the size nor the direction of the coefficient for education changed. 

e. Instead of tertiary education ratio, high school education was used as an alternative education measure. 
However, the coefficient remained negative and insignificant. 

f. Because Western Christian religion (WST) was not showing a significant effect, and we suspected 
some indirect relationship with Western Christianity and teledensity. Therefore, the teledensity 
variable was removed from the model and all coefficients were recalculated. However, the WST 
variable remained insignificant. 

The equation for the complete regression model shows that one percent increase in 
national GNP leads to .30 percent increase in IUR since both the dependent variable IUR 
and the income variable GNP have been logged. An increase by 1 in the civil liberties 
score results in a .195 percent increase in Internet users per 10,000 population. If 
privatization is a year longer, that leads to a .027 percent increased in IUR given that the 
other variables are held constant. Adding one unit to teledensity increases the mean of 
IUR slightly by .001 percent. Increasing education by one unit leads to a .008 percent 
decrease in the mean of IUR. Being predominantly Western Christian leads to .02 percent 
increase in IUR. On other hand, being a predominantly Muslim society leads to a .462 
percent decrease in the number of Internet users per 10,000 population. 






121 



Table 5-7. ANOVA Table for Model 2. ab,c 








Model 2 


Beta 


Std. Error 


Std. Beta 


t 


Sig. 


Constant 


-1.479 


1.158 




-1.277 


.215 


InGNP 


.303 


.148 


.232 


2.051 


.053 


DEM 


.197 


.052 


.359 


3.773 


.001 


PRIV 


.027 


.023 


.085 


1.134 


.270 


TEL 


.001 


.001 


.245 


1.497 


.149 


EDU 


-.008 


.006 


-.097 


-1.332 


.197 


MSL 


-.455 


.175 


-.255 


-2.601 


.017 



a. Dependent Variable: Log Internet users 

b. N=28 

c. Two-tailed 

The second model includes only six variables since the first step of the backward 
selection procedure removed the Western Christianity variable. There are only minor 
changes in the regression coefficients from Model 1 to Model 2. The only change worth 
noting is the increased significance of MSL after the removal of WST. The overall R- 
square for Model 2 remains .92 while the adjusted R-square increases from .896 to .901 
(See Table 5-4, Model 2). This was expected because of the reduced number of variables 
in Model 2. 

At the next step of the backward selection procedure, the telecommunications 
privatization variable was removed. The ANOVA table for Model 3 (Table 5-8) shows 
that the two strongest determinants seem to be democratization and teledensity, as 
indicated by their standardized beta coefficients. The R-square of the regression model 
goes down slightly (from .923 in Model 2 to .918 in Model 3) as does the adjusted R- 
square (from .901 in Model 2 to .899 in Model 3). Still, more than 90 percent of the 
variation in Internet users is explained by the present model. The adjusted R-square also 
remains high. 






122 



Table 5-8. ANOVA Table for Model 3. a ' b ' e 








Model 3 


Beta 


Std. Error 


Std. Beta 


t 


Sig. 


Constant 


-1.444 


1.165 




-1.240 


.228 


InGNP 


.289 


.148 


.221 


1.946 


.065 


DEM 


.199 


.052 


.363 


3.794 


.001 


TEL 


.002 


.001 


.317 


2.085 


.049 


EDU 


-.008 


.006 


-.090 


-1.237 


.229 


MSL 


-.347 


.166 


-.195 


-2.096 


.047 



a. Dependent Variable: Log Internet users 

b. N=28 

c. Two-tailed 



The final model is derived through removing the tertiary education variable using 
the backward selection procedure. Table 5-9 shows the ANOVA table for the final 
model. Model 4. Western Christian religion, telecom privatization, and education have 
been removed from the complete regression model. Thus, the four determinants left in the 
reduced model are income, democratization, teledensity, and Muslim religion. The 
standardized beta coefficients indicate that democratization is most influential among 
those, followed by teledensity, national income, and Muslim religion. This final model, 
Model 4, has an R-square of .912 and an Adjusted R-square value of .897. The final 
model also has the most significant F-test (F=59.77, p=.000) as shown in Table 5-4, 
Model 4. The regression line derived from Model 4 is the following: 

InrUR = -1.683 + .2841nGNP + .222DEM + .002TEL - .347MSL + e 
Interpreting the partial effects of the four remaining variables, it is clear that one 
percent increase in national GNP leads to a .284 percent increase in IUR. An increase by 
1 in the civil liberties score results in a .222 percent increase in Internet users per 10,000 
population. Similarly, an increase by 1 in teledensity increases the mean of IUR by .002 
percent. Finally, being a predominantly Muslim country leads to .347 percent decrease in 
the number of Internet users. 



123 



Table 5-9. ANOVA Table for Model 4. a ' K c 








Model 4 


Beta 


Std. Error 


Std. Beta 


t 


Sig. 


Constant 


-1.683 


1.162 




-1.448 


.161 


InGNP 


.284 


.150 


.217 


1.896 


.071 


DEM 


.222 


.050 


.405 


4.480 


.000 


TEL 


.002 


.001 


.276 


1.841 


.078 


MSL 


-.347 


.166 


-.195 


-2.096 


.047 



a. Dependent Variable: Log Internet users 

b. N=28 

c. Two-tailed 

Which of the four models presented above should be selected as the best model? 

The second model, which included all predictors except the Western Christianity variable 
removed at the first step of the backward selection procedure, had the highest adjusted R- 
square. If we only look at adjusted R-square value then, Model 2 should be considered 
superior to Model 4. The final model-Model 4, however, is more parsimonious, as it 
includes fewer predictors and explains almost as much of the variation in IUR as Model 
2. In fact, the F-tests for Model 4 is highest compared to the previous regression models. 
Based on the F-test statistic, we conclude that the final model—Model 4-could be seen as 
better than the previous regression models. 

The results of the final regression model, again, were derived by a backward 
selection procedure so once a variable was removed, it did not come back into the model. 
Thus, multicollinearity among variables may have affected the selection order of the 
remaining variables. The backward regression, however, helped select the most 
significant determinants of Internet use in the post-communist countries. Due to 
multicollinearity, the individual beta coefficients in the multiple regression analysis 
should be interpreted with caution. However, multicollinearity does not jeopardize the 



124 

explanatory power of the overall model. Limitations of the study are addressed in the 
concluding chapter. 

Among the seven independent variables, income and teledensity are the two 
variables most highly correlated. Putting them together in one model raises the question 
whether there is some indirect relationship between the two variables. However, previous 
literature shows that both factors are important, which makes it necessary to include both 
variables in the model. A possible indirect relationship between income and teledensity 
can be expected. However, a study found that adding an interaction term for these two 
variables did not matter (Dimitrova, In press). 

Tobit Estimates 

The dependent variable in this study— number of Internet users— cannot take on 
negative values. Therefore, IUR is a truncated variable and invites the use of a different 
statistical analysis (Maddala, 1984; Tobin, 1958). Even though there are no serious 
violations of the equal variances assumption in IUR, it is still helpful to conduct a Tobit 
analysis on the same data in order to validate the regression results. Therefore, a Tobit 
estimates were conducted on the final model derived from the backward regression. The 
results of the Tobit analysis are presented in Table 5-10. 

The Tobit estimates presented in Table 5-10 show that all predictors in the final 
model remained statistically significant. The Chi-square value was highest for the 
democratization variable, followed by Muslim religion, GNP per capita, and teledensity. 
The relative magnitude of the Chi-square values resembles closely that of the t-statistics 
in the multiple regression analysis while the significance of the predictor variables is 
even higher. 






125 



Table 5-10. Tobit estimates for final model 



a, b, c 



Model 4 


Beta 


Std. Error 


Chi-square 


Sig. 


Constant 


-1.671 


1.051 


2.528 


.112 


InGNP 


.283 


.136 


4.353 


.037 


DEM 


.221 


.045 


24.386 


.000 


TEL 


.002 


.001 


4.233 


.040 


MSL 


-.346 


.150 


5.316 


.021 



a. The table reports Tobit estimates with Chi-square values and probability levels in last column. 

b. Dependent Variable: Log Internet users 

c. N=28 

As expected, the regression coefficients and the coefficients in the Tobit analysis 
remained almost identical. However, the Tobit table reveals that the standard errors for 
the coefficient estimates went down, including both the intercept and the other four 
variables. The probability levels also went down, compared with the p-values of the final 
regression table, indicating even more strongly that the estimates were not produced just 
be chance. The Log likelihood for normal in the Tobit estimation was -.465. 

The results of the multiple regression analysis, in sum, showed substantial evidence 
for the proposed relationships among the variables. Three of the hypotheses were 
supported, one was partially supported, and three were not supported. Significantly, more 
than 92 percent of the variation in Internet use was explained using the complete 
multivariate model and about 90 percent was explained by the reduced final model. The 
next chapter presents a discussion of the regression results in view of the literature 
review. Suggestions for future research are offered in the final chapter. 






CHAPTER 6 
DISCUSSION 

Internet growth around the globe has been phenomenal. It has been argued that the 
speed of Internet adoption has been unprecedented in the history of communication 
technology. According to Nua's September 2002 data, the current worldwide Internet 
population is 605.60 million (Nua, 2002). Projections for 2004 estimate an increase to 
709. 1 million Internet users around the world (Cyberatlas, 2002). The Internet offers 
potential benefits to nations worldwide in the areas of the political development, 
economic progress, technological advancement, health and education. The World 
Development Report titled Knowledge for Development noted that today even the 
"remotest village has the possibility of tapping a global store of knowledge beyond the 
dreams of anyone living a century ago, and more quickly and cheaply than anyone 
imagined possible only a few decades ago" (World Bank, 1999, iii). 

The Internet has changed world communications and the development of nations, 
yet research on what affects its growth internationally remains inconsistent and 
inconclusive. The main goal of this study was to identify the most significant predictors 
of Internet adoption in the post-communist countries. A brief summary of the results is 
presented below, followed by a discussion of the descriptive statistics and the hypothesis 
testing. 

Overview 

This dissertation proposed and tested a five-dimensional theoretical framework to 
explain the variations in Internet use across the post-communist countries. Three factors 






126 



127 

emerged as critically important: economic, political, and technology/infrastructure 
factors. Cultural factors seemed to exert some impact, but the results were inconclusive. 
These findings suggest that the traditional country-level indicators of economic wealth 
and technological infrastructure are just as important, and maybe more so, in today's 
digital age. They serve as strong determinants of Internet use in the countries of Eastern 
Europe and the former Soviet Union. But democratization level emerges as even more 
important than income and infrastructure in that region. More democratic societies, which 
offer greater freedoms to their citizens, are likely to encourage more extensive use of the 
global network of networks. 

The post-communist countries vary widely in Internet use. Looking closely at these 
28 countries, there is a noticeable gap, which can be considered a regional digital divide. 
But what exactly leads to such higher Internet use in one post-communist country and 
such lower Internet use in another? Some have argued that the answer is national wealth. 
If this were simply a question of income, though, then Slovenia should be the Internet 
leader in the region. However, this is not the case. If it were just an infrastructure issue, 
on the other hand, we should have observed phone-deprived Albania as the Internet 
laggard in the region. Yet this is not the case either. The process of Internet adoption is a 
complicated phenomenon influenced by a multitude of factors. The results of this study 
shed some light on the macro-level indicators that drive Internet growth in the post- 
communist countries in particular. The findings may be applicable to other regions and 
other countries around the globe. Yet it should be underscored once more that this 
analysis focused only on the 28 nations of Eastern Europe, the former Soviet Union, and 



128 

Mongolia. Therefore, applying the five-dimensional theoretical framework proposed here 
should be made within the regional context. 

The results of this study clearly identified four specific factors as critical 
determinants of country-level Internet adoption: democratization, teledensity, national 
income, and Muslim religion. All of these factors except Muslim religion have a positive 
impact on Internet diffusion levels. Being a predominantly Muslim country, however, 
seems to exert a negative impact on Internet diffusion. This can be an indication of strong 
cultural differences associated with countries where Islam is the dominant religion. 
Another possible explanation for the negative relationship could be that, incidentally, the 
Muslim countries in the region have been historically in the "backyard" of the former 
Soviet bloc. The Central Asia republics of the former Soviet Union in particular have had 
less developed infrastructure and lower political autonomy in the past. Thus, the negative 
relationship between Muslim religion and Internet use could be only a reflection of long- 
lasting historic legacies. 

The rest of this chapter goes deeper into interpreting the presence and absence of 
relationships when testing the five-dimensional theoretical framework. First, we briefly 
look at the descriptives and the meaning of Hypothesis 1. Next, we turn to each of the six 
initial predictors and attempt to explain their influence on Internet use in the post- 
communist countries. 

Discussion of Descriptive Analysis 
Regional Variations 

This section briefly reviews the range of variations among the post-communist 
countries across the six variables of interest: national income, democratization, 
telecommunications policy, teledensity, education, and religion. 






129 

The majority of the 28 post-communist countries are lower middle income, a few 
are low income, and only six are classified by the World Bank as upper middle income. 
Only one country-Slovenia— is a high income country according to the World Bank 
classification. Most of the countries in the region, however, remain in the lower middle 
income group. 

Naturally, the countries in the region differ widely in their level of democratization. 
None of the post-communist countries is ranked as completely free or democratic, 
compared with the rest of the world. Nine countries are ranked as mostly free and can be 
considered aspiring democracies. Only one country is absolutely undemocratic- 
Turkmenistan. As expected, that country has lower Internet use than most; it ranks #26 
among the 28 countries. The rest of the post-communist countries are in the middle range 
of the civil liberties ranking, with some having made more progress than others. Belarus 
is an example of a country where political and civil freedoms have been restricted by the 
government. It ranks #23 among the group. 

Telecommunications privatization in the region as a whole has been slow. 
Privatization of the major telecommunications operator was successful in 2 out of 5 cases 
only. Several countries including Estonia and Hungary have successfully privatized their 
PTTs. The majority of countries, however, are lagging behind in the telecommunications 
privatization process. 

Telephone penetration in the post-communist countries is relatively high compared 
with Africa, for instance, but lower than Western European levels. The teledensity across 
the region also varies widely: the country with highest teledensity -Estonia— leads the 
country with lowest teledensity- Albania-by a factor of 16. Mobile phone penetration 



130 

has increased throughout the region, but remains much lower than landline telephone use. 
Yet traditional residential phones remain outdated in terms of connection quality. 

The post-communist countries have been well known for their advanced 
educational system. Traditionally, college attendance in the region has been high. Not 
surprisingly, there is relatively little variation in terms of tertiary education ratio in the 
region. Compared to other regions, the post-communist countries rank higher in relative 
terms. Their mean tertiary education ratio is approximately 28 compared to tertiary 
percentage enrollment of 1 in Angola and Haiti, 1 1 in Iraq, 15 in Brazil, and 16 in 
Mexico. The education level of Western European nations, however, is somewhat higher: 
62 for Norway, 5 1 for France, and 45 for Denmark. 

The most common religion in the region is Eastern Orthodox religion. The other 
two major groups are Western Christians and Muslims. Western Christianity includes 
Protestants and Catholics. It is interesting to note that most of the former Soviet republics 
located in Central Asia are Muslim countries. Compared to other regions, the post- 
communist countries are more uniform in terms of religious composition. A clear 
dominant religion exists and the population is highly homogeneous for the countries in 
this group unlike the United States, for example, where different ethnic minorities and 
various religious affiliations are abundant. 
Growth of Internet Use 

The Internet is one of the fastest growing communication technologies (WIPO, 
2001). It has been adopted globally at an unprecedented pace. Yet, as earlier chapters of 
this dissertation noted, many world regions are still behind in Internet use compared with 
the United States and Western Europe (Daly, 1999; Dasgupta et al., 2001; World Bank, 
1999, 2000). Even though Jupiter Research projects that by 2005, the U.S. share of the 



131 

Internet population will drop to 24 percent (Jupiter Research, 2001a), there is still an 
imbalance between Internet-rich and Internet-poor countries. Indeed, there are huge 
inequalities in Internet access and use around the world (Norris, 2000). 

Some striking figures help illustrate the magnitude of this digital divide. For 
example, Dasgupta et al. (2001) show that in 2000, 90 percent of the world's Internet 
subscribers came from countries with 15 percent of the world population (Dasgupta, et al. 
2001). Developing countries account for only 26 percent of all Internet users, with only 2 
percent of the population in these countries being online (ITU, 2001). These numbers 
illustrate the severe inequality in terms of Internet access across countries worldwide. 

Sub-Saharan Africa is one region where the adoption of Information and 
Communication Technologies (ICTs) has been much slower. Eastern Europe is in the 
middle of the road, faster in adoption of the Internet than African countries, but slower 
than Western European countries. The post-communist countries seem to parallel Internet 
adoption and penetration in Central and Latin American countries. 

Still, the number of Internet users in the post-communist countries remains slim 
compared to that in the United States. Howard et al. (2001) report that the Internet has 
become a vital part of the lives of Americans. Data show that 87 percent of the American 
population uses email and 33 percent uses the Internet to retrieve information regularly. 
In addition, the study reports that 21 percent of Americans use the Internet for major life 
activities such as doing research online about health care and jobs and 9 percent also 
make transactions online (Howard et al., 2001). 

One important finding of a 2000 survey is that the younger Internet users are "more 
likely to . . . gather most kinds of information, and to perform financial transactions 






132 

online" (Howard et al., 2001, 390). They also conclude that there are "a variety of 
demographic factors that affect people's use of the Internet, including gender, age, 
education, income, race and ethnicity. But the most useful predictors of the activities that 
users enjoy online are their length of experience with the Internet and their frequency of 
logging on from home" (Howard et al., 2001, 403). Clearly, the length and frequency of 
Internet use are important predictors of online activities (Howard et al., 2001). 
Demographic variables also continue to be important predictors of Internet use. 

The size of the American online population has increased tremendously over a 
short period of time. Katz et al. (2001) show that the number of users in the U.S. 
increased from 8 percent in 1995 to 65 percent of the population in 2000 (Katz et al., 
2001). In terms of composition, they observe that the number of users age 40 and older 
increased over time. Among a group of demographic predictors, income and education 
emerge as the most salient factors affecting people's awareness of the Internet (Katz et 
al., 2001). They conclude that the digital divide within the USA is shrinking. 

Hypothesis 1 of this dissertation addressed the question whether the average 
number of Internet users in the post-communist countries increased significantly from 
1995 to 1999. The statistical comparison clearly indicated that, indeed, there had been a 
significant increase throughout the region. 

The number of Internet users in the post-communist countries did increase 
significantly during the five-year period from 1995 to 1999. Even though this hypothesis 
only tested the trend for 19 countries (no data for the other nine were available for 1995), 
it is fair to predict that the growth in the rest of these countries will follow a similar trend. 
In fact, looking at the descriptive statistics for Internet users in the region, we expect that 



133 

the countries lagging further behind will exhibit faster adoption and increase use at a 
higher speed. This, of course, can be extrapolated not only from the descriptives, but 
from the diffusion S-curve, which postulates that after a steep increase in the adoption of 
any technology, the diffusion speed decreases and adoption levels off. 

Compared to the rest of the world, the post-communist countries fall in the middle 
range in terms of Internet adoption. On average, about seven percent of the population in 
the post-communist countries is online. Internet indicators reveal an unexpectedly strong 
disparity in usage across the countries. The Economist found that Hungary, for instance, 
is ranked much higher than Ukraine in e-readiness (The Internet's new borders, 2001). 
The Czech Republic and Slovenia are also more advanced than Albania and Azerbaijan 
in terms of Internet access, use, online commerce and other online activities. In 1999, 
Estonia, for example, had 175 Internet hosts per 10,000 people while Albania had only 
0.24. Data on Internet penetration in Hungary show that 7.1 percent of the population was 
online in October 2000 (Minges, 2001). On average, about seven percent of the 
population in the post-communist countries was online as of 2000. This number is 
growing rapidly (CDT, 2000; The Internet's new borders, 2001; ITU, 1999). 

There seems to be a digital divide in the region, with Estonia and the Central 
European countries at the top and the Central Asia former Soviet republics at the bottom. 
The three countries that are leaders in Internet adoption are Estonia, the Slovak Republic, 
and Slovenia. The lowest eight countries are Albania, Azerbaijan, Belarus, Bosnia and 
Herzegovina, Mongolia, Turkmenistan, Tajikistan, and Uzbekistan. There is clearly a 
wide disparity in Internet use across the post-communist countries. The difference in 









134 



Internet users per 10,000 people between the top and the bottom was more than 400 times 
in 1999. 

The Internet has become important in some of the countries, where specific uses 
and projects have emerged. The Internet made possible, for instance, the development of 
medical centers within the region. One project links a major hospital in Ukraine with 
doctors in Finland for real-time medical support. 

Discussion of Hypotheses 2 through 6 

This section provides an interpretation of the results of the multiple regression 
analysis used to test Hypothesis 2 through Hypothesis 6. 

National Income 

The most commonly used predictor of per capita Internet use is probably national 
income, measured as Purchasing Power Parity Gross National Product per capita (PPP 
GNPP) in this study. The second hypothesis stated that higher per capita income would 
contribute to a higher number of Internet users per capita. The regression results strongly 
supported this hypothesis. 

The positive impact of national income on Internet use may come as no surprise to 
some scholars. Previous studies on Internet diffusion across countries have shown the 
importance of economic development for ICT growth. Thus, the results of this study are 
in agreement with Arnum and Conti (1998), Bazar and Boalch (1997), Elie (1998), and 
Hargittai (1999). 

A few previous studies, however, have shown no significance of macro-level 
economic factors. Income per capita was insignificant in a study conducted by the World 
Bank, for example (Dasgupta et al., 2001). The results of this research refute such studies 
and suggest that Internet disparities across the post-communist countries are more than 






135 

just a reflection of infrastructure inequalities, as some studies suggest. The digital divide 
in the region is due, at least in part, to the economic inequalities between the countries. 
Future studies of cross-country Internet diffusion should incorporate national income as 
an important determinant, but take into consideration that it is not the only determinant 
that plays a role in the process. 

It was expected that GNP per capita might turn out to be the variable with strongest 
explanatory power in the final multiple regression model. Some studies have shown that 
national income level is the strongest predictor of Internet adoption (Beilock & 
Dimitrova, 2003; Elie, 1998; Hargittai, 1999). This was not the case in our findings. One 
reason for the relatively lower significance of national income among the predictor 
variables may be partly due to the multicollinearity between GNP and teledensity. The 
implication of such multicollinearity is that these two variables, if entered together in the 
model, may lower each other's significance. 

In this study, national income was the third most significant determinant of Internet 
diffusion, following democratization and teledensity. As Internet connection prices go 
down relative to average wage, the income factor may be further reduced in its 
importance in the post-communist world. This is supported by a comparison of Western 
European nations where income did not appear significant as a predictor of Internet usage 
rates (Beilock & Dimitrova, 2003). It may be expected that the importance of income 
would wane over time. It should be noted, however, that national income level will 
remain an issue in countries where the hourly rate for Internet connection equals or 
exceeds one percent of the monthly salary. 



136 

Democratization 

Level of democratization was used as a political factor in the five-dimensional 
analytical framework. Compared with income and infrastructure factors, democratization 
is not incorporated as frequently in diffusion studies. It was considered an important 
factor in this analytical framework, however. This study predicted a positive relationship 
between the level of civil liberties and Internet adoption levels. The results of the 
multiple regression showed that the higher the level of civil liberties in the country, the 
higher the number of Internet users per capita. 

This finding supports the argument that political factors are critical for Internet 
development. Civil liberties are an indicator of the level of democratization. The process 
of democratization has been particularly important in the region. In fact, the word 
"democratization" is often used synonymously with the word "transition" in the post- 
communist world. Democratization as a broad term also refers to the opening of society 
to the rest of the world and not just to the political and civil freedoms allowed in the 
country. 

Norris (2000, 2001) observed a recurring trend that established democracies around 
the globe tended to have higher Internet penetration. Bivariate correlations clearly 
showed that higher level of democratization and higher Internet use were positively 
related (Norris, 2001). Yet, Norris (2001) found no statistical significance of the 
democratization variable in her world model. In this study, however, democratization 
emerged as the most influential factor in the region of the post-communist countries. This 
could be partially due to the specificity of the region examined in this study. 

Civil liberties appear to be critical for Internet adoption in the post-communist 
countries. The civil liberties composite variable used here includes several areas. One is 



137 

the freedom of expression and belief. The existence of free and independent media then is 
positively related to Internet growth. Another aspect of civil liberties is the freedom of 
assembly and demonstration, in other words, freedom to protest openly. It may not be far- 
fetched to argue that countries which do not allow freedom of assembly are likely to 
restrict Internet use as well. In addition, an independent judicial system, respect for 
human rights, personal autonomy and economic rights are also important civil liberties. 
We infer that countries that restrict such rights are likely to have lower Internet 
penetration. 

These conclusions are supported by Rodriguez and Wilson's (2000) study, in which 
they underscore the significance of political freedoms and civil liberties for the 
technological progress of developing countries. They argue that the existence of a climate 
of democratic freedoms not only facilitates, but also is a requirement for the faster adoption 
of ICTs (Rodriguez & Wilson, 2000). The existence of civil liberties as a necessary 
condition for Internet adoption has been overlooked in the literature so far. 

It is important to note that there may be exceptions to the rule. Some countries with 
undemocratic governments have very high Internet use. Singapore and China are two 
examples of that. While these countries were not examined in this study, it is plausible 
that in both cases high Internet use may be related to systematic government policies to 
encourage use. In the case of Singapore, it is important to note that the country has the 
lowest Uncertainty Avoidance Index (UAI) score in the world (Hofstede, 2001). 
Therefore, some cultural factors may dominate over the level of democratization in 
society in certain cases. 



138 

Telecommunications Privatization 

Many scholars have underscored the significance of government policies when 
discussing Internet adoption. Petrazzini and Guerrero (2000), for example, note the 
importance of telecommunications policy in the Latin American countries. Tanner (1999) 
discusses the significance of telecommunications policy in regard to Western Europe. 
Sallai (2000) and Wolcott et al. (2001) also have noted the importance of policies in the 
telecommunications sector for the growth of Internet use. 

The five-dimensional analytical model proposed that a longer period of 
telecommunications privatization would lead to a higher number of Internet users across 
countries. Privatization was not a significant predictor in this study, however. This 
finding is in accordance with the Kiiski and Pohjola (2001) study, which finds no effect 
of telecommunications policy on country-level Internet use. However, we need to be 
cautious in interpreting that telecommunications privatization has an effect on national 
Internet development. 

One possible explanation for the lack of significance of the telecom privatization 
variable may be simply the distribution of the data points. In other words, the lack of 
importance may be largely due to the fact that many of the values of the policy variable 
equal zero as of the end of 1999. If that is indeed the case, it is likely that the effects of 
telecom privatization will be visible in the long run, and that telecommunications policy 
will show increased importance over time. 

Previous research again in the Western European countries found a strong 
explanatory power in policies regarding the telecommunications sector. Hargittai's 
(1999) research on Internet connectivity among the Organisation for Economic 
Cooperation and Development (OECD) countries is one of the important studies that 



139 



show a significant relationship between Internet use and telecom policy. She showed that 
countries that have allowed free competition, or even some degree of competition, have 
higher Internet penetration than countries with telecom monopolies (Hargittai, 1999). 
Even though the results of this study do not support Hargittai's findings, it is, again, 
likely to see stronger effects of telecommunications policy on Internet use in the near 
future. 

The apparent contradiction between Hargittai's conclusion and the findings of this 
research may be explained by the fact that each study measured telecommunications 
policy in a different way. Hargittai (1999) used the level of competition (monopoly, 
duopoly, competitive market) in the telecommunications sector as a predictor. That 
predictor reflects one aspect of telecom policy—demonopolization. This study, however, 
modeled telecom policy by a privatization variable. It appears that length of privatization 
may be insignificant for Internet adoption in contrast to level of competition in the 
telecom market. We need to distinguish between these two different aspects of 
telecommunications reform, but also bear in mind that different policy dimensions may 
influence Internet use differently in different regions. Hargittai's (1999) study focused on 
OECD countries (Western European nations) while this study examined Eastern 
European countries and former Soviet republics. 

Still, telecom market privatization may play an important role in the post- 
communist world's technological future. A number of previous studies have suggested 
that it is critical to include the level of privatization when studying Internet adoption in 
the post-communist countries (Ellis, 1999; Fish, 1998; Gulyas, 1998; Kuentzel et al., 
2000; Maddock, 1997; Papir & Oleszak, 2000; UNDP, 1999). Even though the effects of 






140 

privatization in general and the main telecommunications operator in particular may be 
yet invisible, their long-term impact in the transition countries needs to be followed. 
Future studies should also consider other measures of telecommunications policy such as 
level of competition in the telecommunications market in addition to length of 
privatization. 

Infrastructure 

Technological infrastructure has often been described as one of the barriers to 
increased Internet diffusion. When there are more telephones in New York City than in 
several African countries combined, the existence of strong cross-country Internet 
discrepancies is hardly a surprise. This study tested the relationship between teledensity 
and Internet use per capita. Even though the relationship between the two variables was 
positive, it was not significant at first in the complete regression model. However, 
subsequent analysis showed that telephone infrastructure emerged as the second most 
important factor in the Internet diffusion process. 

The teledensity variable, even though insignificant in the complete model, 
remained in the final model after backward regression was conducted. There are at least 
two possible reasons for this phenomenon. The most likely reason may be that there is 
some indirect relationship between teledensity and telecommunications privatization, 
both of which are in the full regression model. Conceivably, countries which privatized 
their telecom operator earlier have higher teledensity to begin with, which makes the 
effect of both variables disappear. Therefore, teledensity becomes significant once the 
less influential privatization variable is removed from the regression model. 

Another possible explanation could be the high multicollinearity between GNP and 
teledensity discussed earlier in this study. In cases of such high correlation between 



explanatory variables, it is common to see that one variable increases its significance at 
the expense of the other one and vice versa. Additionally, even though teledensity is not 
statistically significant in the complete model, it still contributes to its overall explanatory 
power. 

Since existing infrastructure significantly affects Internet adoption at the country 
level (Arnum & Conti, 1998; Bazar & Boalch, 1997; Elie, 1998; Gulyas, 1998; Hargittai, 
1999; Lin, 1998; Sadowsky, 1993), variations in Internet use are to be expected between 
countries with different levels of infrastructure development. This study looked 
specifically at telephone infrastructure. The relative degree of difference between 
countries in the post-communist world, however, may be much smaller than the 
differences that exist between Rwanda and France or Morocco and Finland, for example. 
Telecommunication infrastructure varies widely across continents, regions, and countries 
(Daly, 1999; ITU, 2000). The variations are not as stark within the former Soviet bloc 
region, however. Therefore, we expect that the importance of pre-existing telephone 
networks may be higher when comparing all countries in the world. Beilock and 
Dimitrova (2003), among others, have shown that teledensity is a significant determinant 
of adoption, examining a world model of 105 countries. Telephone infrastructure then 
appears to be critical for Internet adoption on a global scale. 

It is important to note that this dissertation is one of the first studies to include 
mobile phones and not just residential phones as part of the technology infrastructure. 
This is an innovative approach, which may be useful in future research because the 
importance of mobile telephone infrastructure is expected to grow over time. The only 
drawback of this approach is that the individual effects on residential versus mobile 






142 

phones cannot be isolated. Thus, we can only conclude that both residential and mobile 
phones are significant in the Internet adoption process. 

Education 

Previous research, for the most part, has shown that educational level is an 
important determinant of Internet use. The results of this regression analysis, however, 
did not support this proposition. In fact, higher education seemed to be related to lower 
Internet use in the region of interest. Even though the results for the education predictor 
were not statistically significant, we need to address not only the lack of significance, but 
also the negative sign of the correlation coefficient between college education and 
Internet use. 

It is important to note that the post-communist countries offer good quality 
education—both at the high school and college level. Even though the 1999 Human 
Development Report underscores that education in the region as a whole has recently 
deteriorated, both in terms of quantity and quality (UNDP, 1999), the basic educational 
level of the general population remains high. Typically, more educated people tend to be 
earlier adopters of technology (Rogers, 1995). Yet we found a negative relationship 
between educational level and Internet use. 

Does education matter? The results of this study are inconclusive. The tertiary 
enrollment ratio used here as a measure of educational achievement was not statistically 
significant. Even more surprisingly, the regression coefficient for that factor was 
negative. This seems contradictory to previous studies that found a positive relationship 
between education and Internet use. There are at least two possible explanations for this 
inconsistency. 



143 

First, it seems that younger people tend to be heavier Internet users (Howard et al., 
2001). This is particularly true in the post-communist countries (Kouznetsov & Bourtsev, 
1996). Rose (2002) notes that in Russia, for instance, more than one-sixth of the people 
under age 29 are Internet users. In other words, age is inversely related to use. Older 
people in general are slower in the adoption of new technologies, such as the Internet. 
Yet, older people tend to be more educated. Being older and more mature means being 
more educated than the younger population, but also, arguably, less likely to use the 
Internet. Therefore, including education without controlling for age may produce 
misleading results. 

Additionally, basic use of the Internet may be simple enough for people with less 
than a college degree. How much knowledge does one need to use the Web? The World 
Wide Web combines text and graphics, and learning the main navigation tools today may 
require little instruction. Email, which is the most popular use of the Internet around the 
world, tends to be user-friendly and easy to use. A better education variable to be used 
could be a specific measure of ICT skills. Finally, UAI tends to be lower among younger 
people. Future research should try to incorporate UAI into the education variable in order 
to avoid possible interaction. 

One of the few studies that supports the results of the current research was 
conducted by Kiiski and Pohjola (2001). These researchers also found no statistical 
significance of the education variable in their analysis of cross-country Internet 
penetration. It is suggested here that educational level is not an important determinant of 
Internet use during the early adoption stage. However, it may become more significant at 
later stages of the Internet adoption process. 



144 



Religion 

The study also hypothesized that differences in dominant national religion would 

affect the number of Internet users per capita. The results of the regression analysis 
showed partial support for this hypothesis. It seemed that Muslim religion had a 
significant effect on Internet use in the post-communist countries, but Western 
Christianity did not. 

Religion was incorporated in this study as a cultural measure. The dominant 
religion in the country was measured as Eastern Orthodox Christianity (the most common 
religion in the group), Western Christianity (Catholic or Protestant), and Islam. It was 
argued that these three religions were substantially different from one another (Fish, 
1998), and that religion was a reflection of substantial cultural differences across the 
countries (Hofstede, 2001). 

Based on the two dummy variables employed in the study, some interesting 
findings emerged. First, Western Christianity did not seem to have a statistically 
significant effect on the level of Internet adoption in the country. Its regression 
coefficient, however, was positive. In contrast, the coefficient for Muslim religion was 
negative and statistically significant. This clearly indicated a negative correlation 
between Internet use and Muslim religion. The causal relationship between the two 
variables, however, is not so clear. 

The following countries from the population of interest are predominantly Muslim: 
Albania, Azerbaijan, Bosnia and Herzegovina, Kazakhstan, Kyrgyzstan, Tajikistan, 
Turkmenistan, and Uzbekistan. Incidentally, the Muslim countries in the sample share 
some historic legacies. Six of them are former Soviet republics, five of which are located 
in Central Asia, and one— Azerbaijan— in the Caucuses. Arguably, these countries 



145 

received less technological support and less infrastructure investment while members of 
the Soviet Union. In fact, the telephone infrastructure in some of the Central Asia 
republics is still quite poor compared with 21 st century standards. Therefore, it is 
conceivable that Muslim religion was significant in this study because it captured historic 
inequalities in the realm of technology and national infrastructure. 

To explore the question whether religion serves as a substitute for infrastructure in 
the multiple regression, a model without the teledensity variable was tested. We 
performed this regression with the expectation that Western religion would remain 
significant when the teledensity variable was not included if the first explanation 
suggested above was true. However, this was not the case. Muslim religion was 
significant and negatively related to Internet use whereas Western religion was removed 
from the model even when teledensity was excluded. It may be conceivable that Western 
religion is positively related to democratization and educational level in addition to 
teledensity. But it seems more likely that perhaps religion parallels broader cultural 
differences among the countries of interest. 

There is another possible explanation for the negative relationship between 
predominantly Muslim societies and per capita Internet use. It is possible that religion is 
correlated with some other cultural factor that is negatively related to Internet adoption. 
Thus, religion may have a spurious relationship with Internet use and is most likely an 
indication of some strong cultural differences between Muslim and Christian societies. 
One such cultural factor could be, for instance, overall openness of society to new 






146 



technologies. This is similar to Hofstede's dimension of uncertainty avoidance (Hofstede, 
1980,2001).' 

Hofstede developed four dimensions of culture: power distance, uncertainty 
avoidance, individualism and collectivism, and masculinity and femininity. The 
dimension which relates to the adoption of innovations in general and the Internet in 
particular is the uncertainty avoidance index (UAI) (Hofstede, 2001). Hofstede (2001, 
161) defined uncertainty avoidance as "the extent to which the members of a culture feel 
threatened by uncertain or unknown situations." Research shows that UAI is negatively 
correlated with the adoption of new media and use of the Internet (Hofstede, 2001). 

Dominant religion seems to reinforce the values that led to its adoption in a 
particular country. Also it is important to note that religion is listed as one of the three 
major ways in which society copes with uncertainty. High uncertainty avoidance 
countries resist innovations and are more resistant to changes in general. In contrast, "low 
UIA countries tend to have more open-minded mentality, in searching for information 
and in accessibility to innovation" (Hofstede, 2001, 170). Religion may not cause 
uncertainty avoidance per se— but both may have a common cause. Hofstede notes that 
"an established religion reinforces the values that led to its adoption, however, 
confirming either strong or weak uncertainty avoidance" (2001, 177). 

For instance, two Muslim societies— Turkey and Pakistan-are ranked very high in 
UAI. This suggests these societies as a whole will be less receptive to new technologies. 
At the other end of the spectrum are Singapore (with the lowest UAI score), Denmark 



However, no data exist on the ranking of the post-communist countries according to Hofstede's cultural 
dimensions, with the exception of former Yugoslavia. Hofstede also notes that the uncertainty avoidance 
ranking of Yugoslavia is not highly reliable. Today Hofstede's questions are included in the European 
Media and Marketing Survey that is administered in the member countries of the European Union. 






147 

and Sweden. These countries have lower uncertainty avoidance, and anecdotal data show 
that they are among the leaders in Internet use in the world. 

In either case, this study partially supports the idea that culture plays a major role in 
adopting technological innovations. However, culture is a very complex concept, as many 
authors have noted (DiMaggio, 1997; Hofstede, 1980, 2001; Sondergaard, 1994; Tayeb, 
1994). 

Therefore, it is difficult to capture all dimensions of culture. Our observations and 
data analysis showed that, controlling for income and infrastructure as well as for other 
factors, Muslim religion was negatively related to Internet use whereas Western 
Christianity had no effect. It would be necessary to test the effects of religion on a 
worldwide model before making conclusive remarks in this regard. In addition, it would 
be desirable to include other cultural variables in future studies of Internet diffusion, 
when data permit. 

Refined Conceptual Framework 

This study proposed a five-dimensional theoretical framework for explaining cross- 
country adoption of the Internet. The overall conceptual model included economic, 
political climate and policy, infrastructure, audience, and cultural factors as determinants 
of adoption. The study showed that three of these factors-namely economic, political, 
and infrastructural-play an important role in the Internet diffusion process in the post- 
communist countries. There was partial support for the influence of cultural factors, as 
revealed by the effect of religion. The policy variable used here— telecommunications 
privatization-did not appear significant. Finally, the importance of audience 
characteristics measured by education in particular was not supported. 



148 

The four specific variables that remained highly significant in this study were per 
capita income, democratization, teledensity, and Muslim religion. These four predictors 
explained a large amount of variation in Internet use in the post-communist countries. As 
expected, higher income, democratization, and teledensity led to higher Internet adoption 
levels. These highly significant predictors of Internet adoption are likely to remain 
important in the future. Being a predominantly Muslim country seemed to affect Internet 
use negatively whereas being a predominantly Protestant or Catholic country had no 
effect. This partial support for the influence of religion points to the potential impact of 
certain cultural predispositions. However, better measures of cultural differences need to 
be developed in order to gather more evidence of their influence on Internet adoption. 

Surprisingly, the telecommunications policy variable was not significant in this 
study. However, this finding may have been driven by the specifics of the data and the 
brief period of time over privatization was examined. It is expected that the privatization 
policies would show an effect in a time-series analysis over an extended period. 

It is also conceivable that a different policy measure— for instance, telecom market 
competition— may be a better determinant of Internet adoption. This study measured only 
one aspect of telecommunications policy— privatization of the national 
telecommunications operator. Privatization by itself may not be sufficient for bring about 
demonopolization in the telecom sector. Both privatization and demonopolization are 
considered important aspects of telecom liberalization in general. 

Finally, we found educational level to be irrelevant for Internet adoption in the 
post-communist countries. It is conceivable that this is an artifact of the relatively small 
variation of the education variable across these countries. Another possible reason is that 









149 

the early adopters of any technology tend to be highly educated. Therefore, it is likely 
that the early adopters in each of the post-communist countries are the highly educated 
elite to begin with, so looking the average number or the whole population may be 
misleading. If this is indeed the case, the influence of education is likely to be more 
visible at later stages of Internet adoption when a higher percentage of the population 
joins the online community. 

The most convincing argument for the lack of significance of education seems to be 
its inverse relationship with age. Younger people tend to have lower education but seem 
to be the more likely Internet users. It is also important to point out another possibility: 
overall educational level may be misleading because it is not education per se that we 
want to capture, but rather the specifically Internet or computer skills of the population. 

Some clarification is necessary regarding the role of audience factors in the Internet 
adoption process. An important audience variable is English language proficiency. 
Considering that the majority of Internet content today is in English and that many 
operating systems and software programs (such as Microsoft Windows and Internet 
Explorer) are available only in English in the countries of interest, this variable is needed 
as a determinant of adoption, even though data for it were not available at this stage. 
Thus, future studies should include English language proficiency and test for its effect on 
national Internet usage levels. 

This dissertation examined only the effects of macro-level determinants. Of course, 
there are other factors that affect diffusion at the individual and institutional level. Norris 
(2001) conceptualized Internet diffusion to be studied at the following three levels: 
macro, meso, and micro level. An extension of this research should zoom in on the 






150 

countries of interest and determine the effect of meso- and micro-level factors. These, 
again, may become more relevant at later stages of adoption. 

In sum, the results of this study suggest that even though economic factors are 
important for Internet adoption in the post-communist countries, infrastructure, political 
and cultural factors do matter as well. The findings of this dissertation have important 
implications not only for the post-communist counties, but for nations across the globe. 
These theoretical and practical implications are discussed in the final chapter. 






CHAPTER 7 
CONCLUSION 

The global digital divide has become a concern for policymakers at both the 
national and international level. Kofi Annan, the United Nations Secretary-General, 
warned about the possibility that the poor of the world could be excluded from the 
information revolution: "These people are deprived of many things: work, shelter, health 
care, and drinking water. But to be isolated from the major telecommunication services 
now is an equally grave deprivation, and chances to remedy this situation are shrinking" 
(Annan, 1999). The growing digital divide is even more disconcerting because the gap 
between the developing and the developed world in information and communication 
technology adoption is greater than the traditional gaps of income and welfare (Daly, 
1999; Rodriguez & Wilson, 2000). As explained below, the results of this study can be 
used by the lesser connected countries to improve their Internet connectivity and thus 
help bridge the gap between the Internet-rich and Internet-poor world. 

Conclusions 

This study developed a five-dimensional theoretical framework to explain the 
variations of Internet use in the post-communist countries. Based on the findings of the 
study, we conclude that economic, political, and technology/infrastructure factors 
positively affect Internet use in the region. Cultural factors also seem to have an impact, 
with Muslim religion adversely affecting Internet use. This negative relationship, 
however, may be, coincidentally, a reflection of historic inequalities between the post- 
communist countries. 



151 



152 

Using multiple regression to test the five-dimensional framework proposed in 
Chapter 3, it was determined that democratization, telephone infrastructure, and national 
wealth were the three most important factors that affect Internet use in the post- 
communist countries. Other determinants suggested in the literature did not appear to 
influence Internet use in the region. Having predominantly Western Christian religion 
seemed unrelated to Internet adoption. National policy— and length of 
telecommunications privatization in particular— did not appear significant in the analysis 
either. Additionally, college education was not found to be related to Internet use in the 
post-communist countries. Contrary to expectations, higher level of education did not 
predict higher Internet use in these countries. 

Three of the five factors proposed in the Internet diffusion framework were found 
to be important. On theoretical grounds, we can derive a refined conceptual framework 
based on the empirical results of the study. The new framework includes (1) economic; 
(2) political; and (3) infrastructure factors. Cultural factors may be important as well, as 
evidenced by the significance of Muslim religion. Including religion or some other 
cultural measure is recommended as a fourth component. A measure similar to 
Hofstede's uncertainty avoidance may be more revealing in capturing cultural differences 
across nations than religion. Finally, it was observed that national income and telephone 
infrastructure are highly correlated, so looking at the effects of one and disregarding the 
other would be a mistake. 

The variables which were not significant in this study were telecommunications 
privatization and educational level. It is recommended to find better proxies for the 
effects of telecommunications policy and education, respectively. We conclude that the 



153 

former should also be measured by telecommunications demonopolization or level of 
competition in the telecom sector and the latter should capture not education per se but 
specifically Internet training and ICT skills. 

Finally, it is expected that the effects of income and infrastructure will wane over 
time. On the other hand, the weight of cultural factors is likely to increase at the later 
stages of the Internet diffusion process, as suggested by Beilock and Dimitrova (2003). 

Implications 

This research has important theoretical and practical implications. The findings of 
this dissertation can be of value to at least three major constituencies: policymakers, 
business companies, and the academic community. 
Theoretical Implications 

The findings of this dissertation contribute to the academic research by (1) offering 

a descriptive analysis of Internet use; (2) showing which factors affect Internet adoption 
in the post-communist world; (3) developing a comprehensive model of Internet diffusion 
that can be applied to other countries and regions of the world; and (4) extending 
diffusion of innovations theory to the country level and showing which of its 
generalizations apply to Internet adoption in the post-communist countries. 

This dissertation fills a gap in the literature because it offers an extensive 
description of Internet adoption in the post-communist countries. No previous studies 
have examined Internet variations in the region in such detail. This study is one of the 
first to show clearly the existence of a digital divide not only around the world, but also 
among the post-communist countries. 

Further, the dissertation identifies a specific set of factors that affect Internet 
adoption in the post-communist world. Thus, it contributes to the body of literature on 



154 

cross-country Internet diffusion by shedding light on the determinants of the diffusion 
process in a specific region of the world. The results clearly show that economic, 
political, and infrastructure factors measured by per capita income, level of 
democratization, and teledensity respectively, influence Internet use in the region. There 
has been a void in the literature on Internet adoption in countries other than the 
industrialized countries of the world, and our findings fill this void. 

Probably the main theoretical contribution of the dissertation is that it develops a 
more comprehensive model of cross-country Internet diffusion than any previously tested 
models. The proposed five-dimensional model is broader and more accurate than earlier 
bivariate models. In addition to the economic and infrastructure variables tested in 
previous studies, it adds the variables of culture and democratization. This 
comprehensive model of Internet diffusion also incorporates audience characteristics and 
policy variables, which have been suggested in previous studies, but rarely included in a 
multivariate analysis. Another advantage of this five-dimensional framework is that it can 
be easily applied to other countries and regions of the world and not only to the post- 
communist countries. 

Finally, the dissertation used some of the generalizations of the diffusion of 
innovations theory and applied those to the country level of adoption. Our results showed 
that income and education as well as culture can be translated and used not only at the 
individual but also at the societal level of adoption. However, a more appropriate 
measure of education than college education may need to be developed when studying 
Internet diffusion. 






155 

Applied Implications 

Policymakers at the local, national, and international levels can use this study to 
develop or modify policies aiming at accelerating Internet use in the former Soviet bloc 
countries. The results of the study can be used as guidelines for Internet diffusion in other 
countries as well. Governments, non-governmental organizations (NGOs), and 
international organizations can benefit from the empirical results of this study, assuming 
that these organizations have a vested interest in promoting Internet development in the 
so-called transition economies of Eastern and Central Europe and the former Soviet 
Union. 

The findings of this research are particularly revenant to NGOs and international 
organizations that are trying to understand and promote Internet adoption around the 
world. As Rodriguez and Wilson (2000) note, multilateral and bilateral support for 
information and communications technology activities in developing countries is warranted 
because of the increasing global technological gap. Organizations such as the Open Society 
Foundation, the International Telecommunication Union, the United Nations, USAID, 
and the World Bank may benefit from the results of this dissertation and use them as 
guidelines for their international Internet-related projects. 

The study examined Internet adoption in the post-communist countries. The results 
identified three main factors— democratization, infrastructure, and national wealth—that 
facilitate Internet diffusion at the country level and suggested other factors that serve as 
barriers. The results demonstrated not only the direction, but also the strength of 
relationships between these factors. 

The level of democratization emerged as the most important determinant of Internet 
adoption relative to the other predictors in the framework. This finding may be deemed 



156 

relevant to policymakers because it shows that limiting democratic freedoms affects 
Internet adoption negatively. On the other hand, encouraging free expression, 
independent media, and freedom of assembly can lead to increased Internet penetration. 

The findings of this dissertation also confirmed the importance of teledensity in the 
process of Internet adoption at the global level. Teledensity emerged as the second most 
important determinant after level of democratization. Organizations such as the World 
Bank that deal with inequality around the world may base their future projects on the 
knowledge that infrastructure gaps still serve as major barriers to Internet adoption, even 
in the post-communist countries. 

Consistent with previous research, national income was found to be significant for 
country-level Internet adoption. It was the third most important predictor in this study. 
One way to increase adoption then would be to increase the wealth of the country. This 
finding has ramifications for national governments and loan granting agencies at the 
national and international level. 

The practical nature of this study also makes it useful to the business community. 
Companies in Internet-related businesses that plan expansion to the post-communist 
countries can find the descriptive results of the study useful. The study could help them 
make strategic decisions on which country to enter first (e.g., Estonia would be a good 
choice since it is the leader in Internet adoption in the region). In addition, this 
dissertation can be helpful to local Internet companies and Internet service providers 
(ISPs). The ISPs can adjust their pricing structures accordingly based on the finding that 
economic factors serve as a barrier to Internet diffusion. Finally, the study has potential 
utility to market research companies that measure and forecast Internet levels at the 









157 

international arena. Such companies can compare the speed of adoption in this region to 
other regions in the world and possibly make some predictions when the Internet is likely 
to reach universal penetration in the post-communist countries. 

The potential benefits of Internet adoption and use were discussed at length earlier 
in the manuscript. Probably the most amazing contribution that the Internet brings to 
people and organizations worldwide is the quick, cheap, and seamless transfer of 
knowledge. "Knowledge is critical for development, because everything we do depends 
on knowledge" (World Bank, 1999, 16). The Internet provides a venue for any individual 
to learn about a multitude of topics—from successful business competition strategies to 
how to treat infant illnesses. Therefore, increased Internet access is likely to bring more 
independence and knowledge to people of all walks of life. 

Some studies have shown concern that the benefits of the Internet would be reaped 
only by the highly developed nations and that less developed countries would gain less 
from increased Internet use. Roller and Waverman (2001), for example, argue that 
increases in telecommunications infrastructure lead to higher growth among more 
economically developed countries, such as the OECD countries. The growth effects are 
expected to be lower in less developed countries (Roller & Waverman, 2001). Daly 
(1999) notes that, paradoxically, "those who have to get most Internet benefits, and those 
who have the greatest needs have least access to the new technology." Further studies 
should examine whether wealthier countries gain more benefits from new technology 
adoption than less developed countries. 

Even if this difference turns out to be true, there is still a bigger danger of leaving 
the developing countries completely behind in the information revolution (Rodriguez & 



158 

Wilson, 2000). That can lead to further increase knowledge gaps (World Bank, 1999). On 
the other hand, if developing nations institute policies and establish institutions to help 
Internet adoption, then can catch up with the industrialized world (World Bank, 1999). 

A report by the European Commission (2001) on the digital divide notes that the 
benefits from increased ICT use may be more qualitative and are not as tangible. 
Therefore, it is difficult to find hard numbers to support the argument of how beneficial 
the Internet is. Some of the areas in which the Internet had been considered a positive 
force are economic development, the political process, leapfrogging, and social life. 

The Internet affects the economic situation in a country as it facilitates international 
trade, lowers production and distribution costs, optimizes productivity within and 
between companies. In terms of contributions to political sphere, the Internet allows 
people to speak publicly and publish information to wider, global audiences. In addition 
to serving as a regulation-free information source, the Internet provides an avenue for 
political communication and mobilization. 

Leapfrogging is the ability of countries less developed technologically to skip 
generations of intermediate technology and adopt the latest one—in this case, the Internet. 
The adoption of the latest technology is considered beneficial to the country. Arguably, it 
serves to facilitate catch-up with more technologically advanced societies. 

The Internet also affects social life. Online applications such as chat rooms and 
email bring people of all nationalities and ethnic backgrounds closer together. These 
online applications redefine social relationships within countries. The effect of virtual 
communities are expected to increase in the future. 






159 



Increased Internet use also has certain ideological implications for the national 
population. The Internet has been seen as a vehicle for transmission of a number of 
ideologies. It allows more democratic communication to take place. Yet it also allows the 
Taliban and other extremist groups to establish worldwide networks and to communicate 
outside national borders. 

This discussion leads to the question: Is technology neural? While it is beyond the 
scope of this study to answer this question, it is important to differentiate between the 
different functions of the Internet. There definitely some beneficial aspects: free and open 
expression online, possibility for political participation, leveling off competition during 
economic transitions, and finally social connectivity. However, some of the negative 
spillover that the Internet can bring to nations are values of consumerism, individualism 
and militarism. Therefore, it is imperative to have a clear distinction between the specific 
uses of the global network of networks, which makes it difficult to predict the exact 
implications of increased Internet use. 

Limitations 

This study has several limitations. Problems with data availability made it 
necessary to include proxy variables for a number of factors identified in the literature 
review. In particular, cost of Internet connection and culture had to be measured with 
proxy variables. English-language proficiency statistics were unavailable at the time of 
this research. Lack of data is a common problem in the post-communist countries, where 
no reliable regional comparative data sources exist (Fish, 1998). 

In an ideal world, social science researchers will not have to deal with issues of 
nonexistent or unreliable data. In reality, we choose the best data available to us when 






160 

conducting our research. However, it is important to understand the limitations of the 
data. 

Validity 

It is critical to assess the validity of the study to assume no systematic bias was 

introduced into the results. Several forms of validity exist. The two broad types of 
validity-internal and external validity— are reviewed below. 

Internal validity 

Internal validity refers to the question whether all variables, measures, statistical 

procedures, and inferences were sound and robust. Babbie (1995) discusses four types of 
internal validity: face, content, criterion, and construct validity. Face validity in a study is 
gained by careful inspection of the concepts and their measures. Basically, it examines if 
the measures and the results of the study seem appropriate at face value, which is true in 
this research. All variables, test procedures, and findings of the study have high face 
validity. 

Content validity establishes that the measure covers the full range of concept's 
meanings. Content validity is high for all variables used in this study except for culture. 
As aforementioned, culture is a complex phenomenon, and its meaning involves more 
than national religion. Lack of data for the uncertainty avoidance index of Hofstede 
prevented us from using that arguably more appropriate measure of cultural differences 
between societies. It was beyond the scope of this study to develop and estimate other 
measures of culture. 

Criterion or predictive validity refers to the questions whether scores obtained on 
one measure can be accurately compared to those obtained with an already validated 
measure of the same phenomenon. The predictive validity of our measures is high 



161 



because they are based on previous research and the results are consistent with earlier 
studies. 

Finally, construct validity applies to situations where there is no clear criterion for 
validation purposes of a particular variable. In this case, a different measure related to 
other measures already in theory is the use of teledensity as a combination of both 
residential and mobile phones. Previous Internet adoption studies have incorporated only 
residential phones as an infrastructure variable. The increasing significance of mobile 
phones in the post-communist countries made it necessary to construct this new 
infrastructure variable. 

The internal validity of the study overall is high because all measures were sound 
and regression assumptions were met. One concern may be the relatively small number of 
observations/countries used in this exploratory study. As explained earlier, however, the 
study incorporates the population of interest-all 28 post-communist countries. 

The dependent variable used to measure Internet adoption within a country was 
Internet users per 10,000 as reported by the International Telecommunication Union. It is 
very difficult to estimate how many people are using the Internet within a country 
(Castells, 2001; Daly, 1999; Press, 1997; Zook, 2000, 2002). One of the challenges for 
developing countries in particular is that people often use the Internet from cyber cafes, 
which is unaccounted for by domain-based measures. Also, current Internet data 
collection methodologies publish different figures of Internet use, which suggests that 
they do not provide completely reliable data. The data on number of Internet users per 
capita, therefore, need to be considered only an approximation of the actual number of 
Internet users in the country. 












162 

Some of the predictor variables point to other issues that need to be addressed. The 
Gross National Product variable (GNP) by definition reflects only officially reported 
economic data. However, UNDP (1999) underscores that shadow economies, which 
remain mostly unreported, are a substantial part of the national economies of the post- 
communist countries. For example, the shadow economy as percentage of GDP was close 
to 17 percent in Bulgaria and over 19 percent in Russia in 1996 (UNDP, 1999). The 
results of this study, however, are based only on officially reported GNP statistics. 

Somewhat surprisingly, education was not significant in this study. It appears that 
the difference between Tajikistan and Slovenia in educational level may be less 
pronounced than the difference between Tanzania and the Netherlands, for instance. The 
lack of significance may be also a result of the way education was measured-as tertiary 
education ratio. 

It was unexpectedly difficult to locate English language proficiency data. Data on 
percentage of high school students studying English exist, but only for the current and 
future members (candidate countries) of the European Union. Therefore, English 
language proficiency could not be included as an explanatory variable in this analysis. It 
is suggested, however, that future studies of cross-country Internet diffusion should 
incorporate English language as a determinant of Internet adoption. 

Multicollinearity-the high correlation between several independent variables-was 
an inherent problem in this research. Multicollinearity was highest between GNP and 
teledensity, but the other variables also showed moderate degree of multicollinearity. The 
backward regression method used in this study reduced the problem of multicollinearity. 
Still, the individual regression coefficients should be interpreted with caution. 



163 

External validity 

External validity is the extent to which the results of a study can be generalized 
across populations, settings, and time. This study has low external validity. The predictor 
variables that emerged as significant for the post-communist countries may not be as 
significant for another set of countries or another region of the world. However, it is 
important to note that these predictor variables would still be applicable for another group 
of countries, but their relative effects on Internet use may vary. 

The external validity is also low in terms of applying this framework to a different 
point in time. It is expected that other variables may become more relevant to global 
Internet adoption at different points in time. Structural variables such as income and 
teledensity may be more significant during the early stages of Internet adoption whereas 
the audience factors may become more critical at later stages. Socioeconomic factors tend 
to be more important at earlier stages of Internet adoption. Also, income and 
infrastructure as well as democratization level (the external factors) are likely to act as 
stronger barriers to adoption than cultural predispositions. 

The study captured Internet adoption at one point in time. Relationships between 
variables may change at different stages of the adoption process. It is important to 
underscore the dynamic nature of the Internet adoption process. Any research findings 
become outdated quickly because ICT use is increasing dramatically in all regions of the 
world (Rodriguez & Wilson, 2000; World Bank, 2001). 

This study examined only country-level factors that affect Internet use. Other 
factors may be influential at the individual or at the institutional level (Arquette, 2002; 
European Commission, 2001; Norris, 2001). Norris (2001), for instance, proposed the 
Internet Engagement Model, in which Internet adoption is to be studied at the macro 



164 

(country) level, the meso (institutional) level, and the micro (individual) level. Again, this 
study focused only on the macro level and examined only country-level factors that affect 
Internet adoption. 

Even though the proposed model of Internet adoption was very comprehensive, it 
did not include every single possible variable that may have an effect on Internet use. 
Rather, the study explored the significance of a set of variables considered most 
important for Internet adoption at the country level at this stage of the adoption process. 

This study is mostly cross-sectional, which limits the applicability of the findings 
for future stages of Internet diffusion. Internet diffusion is a process and not a static 
phenomenon. However, this study captures the adoption process in the post-communist 
countries at one point in time. 

Reliability 

Reliability refers to the question if the same technique applied to the same data 
yields the same results each time. Reliability does not seem to be an issue when using 
secondary data as was the case in this study. There are no sampling issues involved 
because the data gathering methods of the original sources are highly reliable. Since the 
statistical methods were robust, we assert that the study has produced reliable results. 

The interpretation of results in multiple regression always brings the question of 
causality versus correlation. Clearly the correlation coefficients show a strong 
relationship between the aforementioned independent variables and the dependent 
variable, but is that relationship causal? Babbie (1995) lists three conditions that must be 
present in order to claim a causal relationship: (1) time sequence; (2) correlation between 
cause and effect; and (3) no other possible third variable that causes both the dependent 
and independent variable. 



165 

Close examination of the variables in the current study shows that the first two 
conditions are met. Let's take religion as an examine and test each of the three criteria. 
Clearly, religion has come into existence before Internet use. Second, the correlation 
between religion and Internet use is high, as determined by the dummy variable the 
regression analysis. The third criterion is more difficult to test. Arguably, there is no 
other variable that affects both religious predispositions and likelihood of Internet use. 

Suggestions for Future Research 

The Internet is one of the most fascinating new technologies of all time, and 
research on its adoption around the world is growing. This dissertation explained cross- 
country determinants of Internet adoption in the post-communist countries. Future 
research should focus on adoption at the individual and organizational levels to see if 
different determinants play a role. In addition, future research should examine if the 
Internet is used differently by different individuals within the post-communist countries. 
In other words, studies should identify individual demographic characteristics as well as 
personality factors that affect adoption at the individual level in the post-communist 
world. In this regard, more qualitative research of Internet adoption and use within the 
post-communist countries is warranted. 

Another venue for future research would be to isolate better proxy variables for the 
broad set of factors suggested in this analysis. First, it would be desirable to incorporate 
other cultural variables in addition to religion and test their effect on country-level 
Internet adoption. Other measures of telecommunications policy such as level of 
competition or Internet regulation should also be tested in future research. Future studies 
of cross-country Internet diffusion should incorporate English language as an audience 



166 

factor. A better educational variable that should be tested as a determinant of Internet 
adoption in the future is some specific measure of ICT skills in particular. 

Future studies should apply the five-dimensional framework proposed here to a 
world model of Internet adoption. It will be interesting to see if there are any regional 
differences in the effects of the predictor variables when applied to all countries. Such 
studies may help isolate a set of variables that are universally important in the Internet 
diffusion process. 

This was mostly a cross-sectional study of Internet use in the post-communist 
countries of Eastern Europe and the former Soviet Union. Future studies should also 
examine the adoption process in the region longitudinally. A comparative study between 
Western and Eastern European countries is another venue for future research. Such a 
comparative study would possibly allow researchers to identify additional cultural 
predispositions that influence Internet adoption. Finally, a comparative study between 
Western and Eastern European countries longitudinally should also be conducted. 

The importance of the Internet for national development is unquestionable. Today 

the significance of communication technology is more crucial than ever. The main 

message in a recent World Bank report reads: 

Although traditional channels of communication will remain important, the new 
information and communications technologies hold great potential for broadly 
disseminating knowledge at low cost, and for reducing knowledge gaps both within 
countries and between industrial and developing countries. (World Bank, 1999, 56) 

This potential of the Internet makes it crucial to understand the driving forces of its 
adoption at the country level. 

The transition countries of Eastern Europe and the former Soviet Union have 
undergone a unique period of transition since the end of the Cold War. This transition has 



167 

proved to be all but easy. A Freedom House report discusses the difficult transition to 
building a civil society, democracy and market economy in the region as a whole 
(Karatnycky et al., 1997). The United Nations also underscores the dramatic 
transformation in the post-communist countries and notes the various costs of transition 
from totalitarianism to capitalist democracy (UNDP, 1999). The transition has led to 
social problems, economic downfall, inflation, and deteriorating educational system, 
among other issues (UNDP, 1999). Transition costs have been so high that some have 
suggested it is more appropriate to call it the great depression rather than a transition 
(UNDP, 1999). 

When discussing the multidimensional nature of the transition process, it is 
important to realize that the transition in the former Soviet bloc will not be completed 
overnight (Fischer et al., 1998). On the contrary, "overcoming institutional legacies and 
building new, effective institutions is a process that, by definition, will take a long time" 
(Karatnycky et al., 1997, 21). Increased Internet access and use—even though hardly a 
panacea— may contribute to making these societies reach their transition goals faster. 
Higher Internet use may be used to help their development during the challenging 
transitional period. 

What this research has shown is that building a more democratic society leads to 
increased Internet use. Democratization emerges as more important determinant of 
Internet adoption than ever before suggested. However, the structural issues of income 
and infrastructure also remain critical for Internet development in the region. Countries 
with lower national income and poor telephone infrastructure have to overcome these 
barriers in order to increase Internet use. But first, a necessary step to enlarging the 



168 

information superhighway in the post-communist countries is to open up their societies 
and increase the political rights and freedoms of their citizens. 









I 






LIST OF REFERENCES 

Agresti, A., & Finley, B. (1997). Statistical methods for the social sciences (3 rd ed.). 
Upper Saddle River, NJ: Prentice Hall. 

Ahmann, L. (1998). Internet access and political participation: Can the Internet play a 
role in strengthening democracy in South Africa ? University of Florida: 
Unpublished Master's Thesis. 

Arnum, E., & Conti, S. (1998). Internet development worldwide: The new superhighway 
follows the old wires, rails, and roads. Retrieved February 17, 2003, from http:// 
www.isoc.org/inet98/proceedings/5c/5c_5.htm 

Arquette, Toby J. (2002). Social discourse, scientific method, and the digital divide: 
Using the Information Intelligence Quotient (IIP) to generate a multi-layered 
empirical analysis of digital division. Northwestern University: Unpublished Ph.D. 
Dissertation. Retrieved February 17, 2003, from http://www.sla.purdue.edu/people/ 
comm/arquette/2in.pdf 

Atkin, D., Jeffres, L., & Neuendorf, K. (1998). Understanding Internet adoption as 

telecommunications behavior. Journal of Broadcasting & Electronic Media , 42(4), 
475-490. 

Atkinson, R. D., & Court, R. H. (1998, November). The new economy index: 

Understanding America's economic transition. Retrieved February 17. 2003, from 
http://www.neweconomyindex.org/ 

Babbie, E. (1995). The practice of social research (7 th ed.). Belmont, CA: Wadsworth. 

Baily, M. N. (2001). U.S. economic performance and the challenge for Europe . Retrieved 
February 17, 2003, from http://www.iie.com/papers/baily0601.htm 

Barua, A., Pinnell, J., Shutter, J., & Whinston, A. B. (1999). Measuring the Internet 
economy. Retrieved February 17, 2003, from http://cism.bus.utexas.edu/works/ 
articles/internet_economy.pdf 

Bazar, B., & Boalch, G. (1997). A preliminary model of Internet diffusion within 

developing countries. Proceedings of the AUSWEB97 Conference, Southern Cross 
University, Gold Coast, Australia. Retrieved May 7, 2000, from http://ausweb.scu 
.edu.au/proceedings/boalch/paper.html 



169 



170 



Bauer, J. M. (1994). The emergence of global networks in telecommunications: 

Transcending national regulation and market constraints. Journal of Economic 
Issues , 28(2), 391-402. 

Beilock, R., & Dimitrova, D. V. (2003). An exploratory model of inter-country Internet 
diffusion. Telecommunications Policy , 27(3-4), 237-252. 

Berg-Schlosser, D., & Siegler, R. (1990). Political stability and development: A 
comparative analysis of Kenya, Tanzania, and Uganda . London: L. Rienner 
Publishers. 

Berners-Lee, T. (1999). Weaving the Web: The original design and ultimate destiny of 
the World Wide Web by its inventor . San Francisco: Harper Collins. 

Bieler, D., & Stevenson, I. (1998, December). OVUM Report: Internet market forecasts: 
Global Internet growth, 1998-2005. Retrieved February 17, 2003, from http://www 
.gsmdata.com/es53061/repovum2.htm 

Bruce, R. R. (1999). Overcoming obstacles to liberalization of the telecom sector in 
Estonia, Poland, the Czech Republic, Slovenia, and Hungary . Washington, D.C.: 
The World Bank. 

Campbell, R. W. (1995) Soviet and post-Soviet telecommunications: An industry under 
reform . Boulder, CO: Westview Press. 

Canning, A. (1997). Privatization and competition in Hungarian telecommunications. In 
D. Ryan (ed.), Privatization and competition in telecommunications (pp. 103-126). 
Westport, CN: Praeger. 

Caselli, F., & Coleman II, W. J. (2000). The world technology frontier. Retrieved 
February 17, 2003, from http://papers.nber.org/papers/w7904 

Caselli, F., & Coleman II, W. J. (2001). Cross-country technology diffusion: The case of 
computers. American Economic Review , 91(2), 328-335. 

Castells, M. (1996). The rise of the network society . Cambridge, MA: Blackwell. 

Castells, M. (2001). The Internet galaxy: Reflections on the Internet, business, and 
society . Oxford: Oxford University Press. 

Center for Democracy and Technology. (2000). Bridging the digital divide: Internet 
access in Central and Eastern Europe . Retrieved February 17, 2003, from http:// 
www.cdt.org/international/ceeaccess/report.shtml 

Christensen, C. (1997). The innovator's dilemma: When new technologies cause great 
firms to fail. Boston: Harvard Business School Press. 



171 



Christensen, C, Craig, T., & Hart, S. (2001). The great disruption. Foreign Affairs , 80(2), 
80-95. 

Central Intelligence Agency. (2000). The world factbook . Retrieved February 17, 2003, 
from http://www.cia.gov/cia/publications/factbook/index.html 

Clarke, G. R. G. (2001, July). Bridging the Digital Divide: How enterprise ownership and 
foreign competition affect Internet access in Eastern Europe and Central Asia. 
World Bank Working Paper 2629, Retrieved February 17, 2003, from 
http://econ.worldbank.org/view.php?topic=14&type=5&id=2239 

Colin Xu, L., Li, W., & Zhen-Wei Qiang, C. (2001). The political economy of 
privatization and competition: Cross-country evidence from the 
telecommunications sector. CEPR Discussion Paper 2825. Retrieved February 17, 
2003, from http://ideas.repec.Org/p/cpr/ceprdp/2825.html 

Comer, D. E. (1995). Internetworking with TCP/IP (3 rd ed.). Englewood Cliffs, NJ: 
Prentice Hall. 

Cortez, M. V. (2000). Internet censorship around the world. Retrieved February 17, 2003, 
from http://www.isoc.org/inet2000/cdproceedings/8k/8k_4.htm 

Cyberatlas. (2002). Worldwide Internet population. Retrieved February 17, 2003, from 
http://cyberatlas.internet.com/big_picture/geographics/article/0,1323,5911_151151, 
OO.html 

Daly, J. A. (2000, September). Will the Internet promote democracy? iMP Magazine , 
Retrieved February 17, 2003, from http://www.cisp.org/imp/september_2000/ 
daly/09_00daly.htm 

Daly, J. A. (1999, May). Measuring the impacts of the Internet in the developing world. 
iMP Magazine , Retrieved February 17, 2003, from http://www.cisp.org/imp/ 
may_99/daly/05_99daly.htm 

Daly, J., & Miller, R. (1998). Corporations' use of the Internet in developing countries. 
Washington, D.C.: The World Bank. 

Dasgupta, S., Lall, S., & Wheeler, D. (2001, March 28). Policy reform, economic growth, 
and the digital divide: An econometric analysis. World Bank Working Paper 2567, 
Retrieved February 17, 2003, from http://econ.worldbank.org/view 
.php?topic= 1 4&type=5&id= 1615 

de Melo, M., & Gelb, A. (1996). A comparative analysis of twenty-eight transition 

economies in Europe and Asia. Post-Soviet Geography and Economics , 37(5), 265- 
285. 

DePrince, A. E., Jr., & Ford, W. F. (1999). A primer on internet economics: Macro and 
micro impact of the Internet on the economy. Business Economics , 34(4), 42-50. 



172 



DiMaggio, P. (1997). Culture and cognition. Annual Review of Sociology , 23, 263-287. 

Dimitrova, D. V. (2002). Internet uses and gratifications: An online survey of Bulgarians 
at home and abroad. International Communication Bulletin , 37(1-2), 36-49. 

Dinkova, D. (1998). Bulgaria's Internet: Nonprofit organizations are at the forefront. 
Economic Reform Today , 3. 

Domanski, H. (2000). On the verge of convergence: Social stratification in Eastern 
Europe . New York: Central European University Press. 

Drohan, M, & Freeman, A. (1997). Winning the language wars: The world speaks. 
World Press Review , 6-8. 

Dryden, J. (1998). Realising the potential of global electronic commerce. The OECD 
Observer , 214. Retrieved February 17, 2003, from http://oecd.org/publications/ 
observer/2 14/ Article6_eng.htm 

El-Nawawy, M. A. (2000). Profiling Internet users in Egypt: Understanding the primary 
deterrent against their growth in number. Retrieved February 17, 2003, from http:// 
www.isoc.org/inet2000/cdproceedings/8d/8d_3.htm 

Elie, M. (1998). The Internet and global development. Retrieved February 17, 2003, from 
http://www.isoc.org/inet98/proceedings/5d/5d_3.htm 

Ellis, F. (1999). From glasnost to the internet: Russia's new infosphere . New York: St. 
Martin's Press. 

Estache, A., Manacorda, M., & Valletti, T. M. (2002, March 21). Telecommunication 
reforms, access regulation, and Internet adoption in Latin America. World Bank 
Working Paper 2802, Retrieved February 17, 2003, from http://econ.worldbank 
.org/view.php?type=5&id= 1 3 1 62 

European Bank for Reconstruction and Development. (1997). Transition report 1997: 
Enterprise performance and growth . London: European Bank for Reconstruction 
and Development. 

European Commission. (2000). Key data on education in Europe, 1999-2000 . 
Luxembourg: Eurostat Press Office. 

European Commission. (2001, March). The digital divide: A research perspective. Report 
EUR 19913. Retrieved February 17, 2003, from http://www.fraggersxtreme.com/ 
lemon/eur 1 99 1 3en.pdf 

Fischer, S., Sahay, R., & Vegh, C. A. (1998, April). How far is Eastern Europe from 

Brussels? IMF Working Paper WP/98/53. Retrieved February 17, 2003, from http:// 
www.imf.org 



173 

Fish, M. S. (1998). The determinants of economic reform in the post-communist world. 
East European Politics & Societies , 12 (1), 31-78. 

Forrester Research. (2000, August 15). Latin culture and climate explains low Internet 
adoption in France, Italy, and Spain. Retrieved February 17, 2003, from http://www 
.forrester.com/ER/Press/Release/0,1769,377,FF.html 

Freedom House. (2000). Freedom House country ratings. Retrieved February 17, 2003, 
from http://www.freedomhouse.org/ratings/index.htm 

Garrison, B. (2000). Online information use in newsrooms: A longitudinal diffusion 
study. Paper presented to the Newspaper Division, Association for Education in 
Journalism and Mass Communication (AEJMC) conference, Phoenix. 

Global Reach. (2000, September). Global Internet statistics. Retrieved February 17, 2003, 
from http://www.glreach.com/globstats/index.php3 

Godwin, M. (1998). Cyber rights: Defending free speech in the digital age . New York: 
Times Books. 

Goode, S., & Stevens, K. (2000). An analysis of the business characteristics of adopters 
and non-adopters of World Wide Web technology. Information Technology and 
Management , 1, 129-154. 

Gospic, N., Jankovic, M., & Odadzic, B. (2000, August). Yugoslav telecommunications 
markets: Vision and Potential. IEEE Communications Magazine , 38(8), 112-116. 

Gray, A., & McGuigan, J. (eds.). (1997). Studying culture: An introductory reader (2 nd 
ed.). London: Arnold. 

Gruber, H. (2001). Competition and innovation: The diffusion of mobile 

telecommunications in Central and Eastern Europe. Information Economics and 
Policy , 13, 19-34. 

Guillen, M. & Suarez, S. (2001). Developing the Internet: Entrepreneurship and public 
policy in Ireland, Singapore, Argentina and Spain. Telecommunications Policy , 25, 
349-371. 

Gujarati, D. (1995). Basic econometrics (3 rd ed.). New York: McGraw-Hill. 

Gulyas, A. (1998). In the slow lane on the information superhighway: Hungary and the 
information revolution. Convergence: The Journal of Research into New Media 
Technologies , 4. Retrieved February 17, 2003, from http://www.cios.org 

Gunarante, S. A. (2001). Global triadization: A theoretical framework for global 
communication research. Paper presented to the Communication Theory and 
Methodology Division, Association for Education in Journalism and Mass 
Communication (AEJMC) conference, Washington, D.C. 






174 



Guthrie, R.A., & Austin, L. D. (1996). Competitive implications of the Internet. 
Information Systems Management , 13(3), 90-91. 

GVU Web Surveys. (1998, October). Retrieved February 17, 2003, from 
http://www.gvu.gatech.edu/user_surveys/ 

Hafner, K., & Lyon, M. (1996). Where wizards stay up late: The origins of the Internet . 
New York: Simon & Schuster. 

Hanson, J., & Narula, U. (1990). New communication technologies in developing 
countries . Hillsdale, NJ: Lawrence Erlbaum Associates. 

Hargittai, E. (1999). Weaving the Western web: Explaining differences in Internet 

connectivity among OECD countries. Telecommunications Policy , 23(10/1 1), 701- 
718. 

Held, D. (1995). Democracy and the global order: From the modern state to 
cosmopolitan governance . Stanford, CA: Stanford University Press. 

Hoelschner, G. (2000, August). Profile of the Czech communications market. IEEE 
Communications Magazine , 38(8), 77-80. 

Hofstede, G. (1980). Culture's consequences: International differences in work-related 
values . Beverly Hills, CA: Sage. 

Hofstede, G. (2001). Culture's consequences: Comparing values, behaviors, institutions, 
and organizations across nations (2 nd ed.). Thousand Oaks, CA: Sage. 

Horvath, J. (2002). Trouble in cyberspace. Telepolis. Retrieved February 17, 2003, from 
http://www.telepolis.de/english/inhalt/te/ 1 2520/ 1 .html 

Howard, P.E.N., L. Rainie, & Jones, S. (2001). Days and nights on the Internet: The 
impact of a diffusing technology. American Behavioral Scientist , 45(3), 383-404. 

International Monetary Fund. (2000a). World economic outlook: Asset prices and the 
business cycle . Retrieved February 17, 2003, from http://www.imf.org 

International Monetary Fund. (2000b). Dissemination standards bulletin board. Retrieved 
February 17, 2003, from http://dsbb.imf.org/ 

International Telecommunication Union. (1999). Challenges to the network: Internet for 
development . Geneva: ITU. 

International Telecommunication Union. (2000). World telecommunication indicators . 
Retrieved February 17, 2003, from http://www.itu.ch/ti/ 






175 

International Telecommunication Union. (2001). Telecommunication regulation and 
privatization data: Country profiles. Retrieved February 17, 2003, from 
http://www7.itu.int/treg/profiles2/cntryprfiles/Build_Guide.asp 

Jamison, M. (1995). A competitive framework for pricing interconnection in a global 
telecommunications market. Denver Journal of International Law and Policy , 
23(3),513-533. 

Jasinski, P. (1997). Competition rules and regulations in telecommunications: The case of 
Poland's intent to join the EU. In D. Ryan (ed.), Privatization and competition in 
telecommunications (pp. 127-148). Westport, CN: Praeger. 

Jisi, W., Yunus, M., Somavia, J., & Mesquita, R. L. (2001). Crossing the 

digital divide: The Internet in China: A new fantasy? New Perspectives 
Quarterly , 18(1), 22-24. 

Jones, H. B. (1997, July). The Protestant ethic: Weber's model and the empirical 
literature. Human Relations , 50(7), 757-798. 

Jupiter Research Press Release. (2001a, January 1 1). Retrieved February 17, 2003, from 
http://www.jmm.com/xp/jmm/press/2001/pr_01 1 lOl.xml 

Jupiter Research Press Release. (2001b, January 18). Retrieved February 17, 2003, from 
http://www.jmm.com/xp/jmm/press/2001/pr_011801.xml 

Karatnycky, A., Motyl, A., & Shor, B. (eds.). (1997). Nations in transit: 1997: Civil 

society, democracy and markets in East Central Europe and the Newly Independent 
States . New Brunswick: Transaction Publishers. 

Katchanovski, I. (2000). Divergence in growth in post-communist countries. Journal of 
Public Policy , 20(1), 55-81. 

Katz, J. E., Rice, R. E., & Aspden, P. (2001). The Internet, 1995-2000: Access, civic 

involvement, and social interaction. American Behavioral Scientist , 45(3), 405-419. 

Kennedy, P. (1998). A guide to econometrics (4 th ed.). Cambridge, MA: The MIT Press. 

Kiiski, S., & Pohjola, M. (2001, June). Cross-country diffusion of the Internet. 

UNU/WIDER Discussion Paper 2001/1 1. Retrieved February 17, 2003, from 
www.wider.unu.edu/publications/dps/DP2001-l 1 .pdf 

Kleinbaum, D. G., Kupper, L. L., & Muller, K. E. (1998). Applied regression analysis 
and other multi variable methods . Boston, MA: PWS-KENT Publishing Co. 

Kouznetsov, A., & Bourtsev, D. (1996). Prospects for the development of the Internet in 
Russia. INET Conference Proceedings . Retrieved February 17, 2003, from http:// 
www.isoc.org/isoc/whatis/conferences/inet/96/proceedings/hl/hl_2.htm 



176 



Kuentzel, D., Sloutski, L., & Sokolov, N. (2000, December). Evolution of 

telecommunication in Eastern Europe. IEEE Communications Magazine , 38(8), 
143-149. 

Lamberton, D. (1997). The new research frontiers of communications policy . 
Amsterdam, The Netherlands: Elsevier. 

Lari, E. F. (2000). International institutions in Eastern Europe: Into the financial breach. 
Harvard International Review , 13(1), 18. 

Leiner, B. M, Vinton G. Cerf, V. C, Clark, D. D., Kahn, R. E., Kleinrock, L., Lynch, D. 
C, Postel, J., Roberts, L. G., & Wolff, S. (1997). A brief history of the Internet. 
Retrieved February 17, 2003, from http://www.isoc.org/internet/history/brief.html 

Lin, C. (1998). Exploring personal computer adoption dynamics. Journal of Broadcasting 
& Electronic Media , 42(1), 95-112. 

Lin, C. (1999). Online-service adoption likelihood. Journal of Advertising Research , 
39(2), 79-89. 

Lin, N. (1993). Diffusion of information technology: A case study of computer network 
and the role of government, industry, and academia in developing the 
Internet/NREN . University of Texas Austin: Unpublished Ph.D. Dissertation. 

Lindstrom, P. (1997). The Internet: Nielsen's longitudinal research on behavioral changes 
in use of this counterintuitive medium. Journal of Media Economics , 10(2), 35-40. 

Maddala, G. (1984). Limited-dependent and qualitative variables in econometrics . 
Cambridge: Cambridge University Press. 

Maddock, R. (1997). Telecommunications and economic development. In D. Lamberton 
(ed.). The new research frontiers of communications policy (pp. 159-175). 
Amsterdam, The Netherlands: Elsevier. 

Madon, S. (2000). The Internet and socio-economic development. Information 
Technology and People , 13(2), 85-101. 

Magyar, B., & Karvalics, L. Z. (2001). "Information society" in Eastern Europe? 

Chances, possibilities, tasks and programs. East European Quarterly , XXXIV, 4. 

Maherzi, A. (1997). World communication report: The media and the challenge of the 
new technologies . Paris: UNESCO Publishing. 

Mahler, A., & Rogers, E. (1999). The diffusion of interactive communication innovations 
and the critical mass: The adoption of telecommunications services by German 
banks. Telecommunications Policy , 23, 719-740. 









177 



Maitland, C. (1998). Global diffusion of interactive networks: The impact of culture. 
Electronic Journal of Communication , 8. Retrieved February 17, 2003, from 
http://www.cios.org 

Malecki, E. J. (1997). Technology and economic development: The dynamics of local, 
regional and national competitiveness (2 nd ed.). Harlow, England: Longman. 

Malecki, E. J. (2000). Knowledge and regional competitiveness. Erdkunde , 54(4), 334- 
351. 

Malecki, E. (2001). The Internet age: Not the end of geography. In Felsenstein, D., & 
Taylor, M.J. (eds.) Promoting local growth: Process, practice and policy . 
Aldershot: Ashgate. 

McChesney, R. (1999). Rich media, poor democracy: Communication politics in dubious 
times . Urbana: University of Illinois Press. 

McElhinney, S. (2001). Telecommunications liberalisation and the quest for universal 
service in Australia. Telecommunications Policy , 25, 233-248. 

McKnight, L. W., & Bailey, J. (1997). Internet economics . Cambridge, MA: MIT Press. 

McLuhan, M., & Powers, B. R. (1989). The global village: Transformations in world life 
and media in the 21 st century . New York: Oxford University Press. 

Media Metrix Global Services. (2000). Retrieved February 17, 2003, from http://www 
.comscore.com/metrix/gs.asp 

Mendoza, M., & Alvarez de Toledo. (1997). Demographics and behavior of the Chilean 
Internet population. Journal of Computer-Mediated Communication , 3(1). 
Retrieved February 17, 2003, from http://www.ascusc.org/jcmc/vol3/issuel/ 
mendoza.html 

Michalis, M., & Takla, L. (1997). Telecommunications in the Czech Republic: The 
privatization of SPT Telecom. In D. Ryan (ed.), Privatization and competition in 
telecommunications (pp. 89-102). Westport, CN: Praeger. 

Minges, M. (2000). Counting the Net: Internet access indicators. Retrieved February 17, 
2003, from http://www.isoc.org/inet2000/cdproceedings/8e/8e_l.htm 

Minges, M. (2001). Internet around the world. Retrieved February 17, 2003, from 
http://www.isoc.org/inet2001/CD_proceedings/G54/Inet2001_100501.htm 

Mitchell, W. J. (1995). City of bits: Space, place, and the infobahn . Cambridge: MIT 
Press. 

Nelson, R. R. (1993). National innovation systems . New York: Oxford University Press. 



178 

Network Wizards Internet Domain Survey. (2000). Retrieved February 17, 2003, from 
http://www.isc.org/ds/ 

Newhagen, J., & Rafaeli, S. (1996). Why communication researchers should study the 
Internet: A dialogue. Journal of Communication , 46, 4-13. 

Nielsen NetRatings. (2000). Retrieved February 17, 2003, from http://www.nielsen- 
netratings.com 

Nielsen NetRatings. (2002). Retrieved February 17, 2003, from http://www.nielsen- 
netratings.com 

Norris, P. (2000). The worldwide digital divide: Information poverty, the Internet and 
development. Paper presented at the annual meeting of the Political Studies 
Association, London School of Economics and Political Science, UK. Retrieved 
February 17, 2003, from http://www.ksg.harvard.edu/iip/governance/psa2000dig 
.pdf 

Norris, P. (2001). Digital divide: Civic engagement, information poverty and the Internet 
in democratic societies . New York: Cambridge University Press. 

National Telecommunications and Information Administration. (1995, July). Falling 

through the Net: A survey of the "have nots" in rural and urban America. Retrieved 
February 17, 2003, from http://www.ntia.doc.gov/ntiahome/fallingthru.html 

National Telecommunications and Information Administration. (1998, July). Falling 
through the Net II: New data on the digital divide. Retrieved February 17, 2003, 
from http://www.ntia.doc.gov/ntiahome/net2 

National Telecommunications and Information Administration. (1999, July). Falling 
through the Net: Defining the digital divide. Retrieved February 17, 2003, from 
http://www.ntia.doc.gov/ntiahome/fttn99/contents.html 

Nua's Internet user surveys. (2002, September). Retrieved February 17, 2003, from http:// 
www.nua.ie 

Oaca, N. (2000, August). Mobile telephony: The main driver of Romanian 
telecommunications! IEEE Communications Magazine , 38(8), 98-104. 

Organization for Economic Cooperation and Development. (1998a, October). Internet 
infrastructure indicators. Retrieved February 17, 2003, from http://www.oecd.org/ 
dsti/sti/it/cm/prod/tisp98-7e.htm 

Organization for Economic Cooperation and Development. (1998b). Internet traffic 
exchange: Developments and policy . Retrieved February 17, 2003, from http:// 
www.oecd.org/dsti/sti/it/cm/prod/traffic.htm 






179 



Paltridge, S. (2000). Local access pricing and the international digital divide. Retrieved 
February 17, 2003, from http://www.isoc.org/oti/articles/1000/paltridge.html 

Papir, Z., & Oleszak, P. (2000, August). The communications market in Poland. IEEE 
Communications Magazine , 38(8), 91-95. 

Perrit, H. H. (1999, February). The Internet as a threat to sovereignty? Thoughts on the 
Internet's role in strengthening national and global governance. Retrieved February 
17, 2003, from http://www.law.indiana.edu/glsj/vol5/no2/4perrit.html 

Petrazzini, B., & Guerrero, A. (2000). Promoting Internet development: The case of 
Argentina. Telecommunications Policy , 24(2), 89-1 12. 

Pew Center for the People and the Press. (1995). Technology in the American Household . 
Washington, D.C.: Pew Center for the People and the Press. 

Pitkow, J. (1996). Emerging trends in the WWW user population. Communications of the 
ACM , 39(6), 106-108. 

Poster, M. (1995). CyberDemocracy: Internet and the public sphere. Retrieved February 
17, 2003, from http://www.hnet.uci.edu/mposter/writings/democ.html 

Poster, M. (2001). What's the matter with the Internet . Minneapolis, MN: University of 
Minnesota Press. 

Prescott, M. B., & Van Slyke, S. (1997). Understanding the Internet as an innovation. 
Industrial Management and Data Systems , 97(3), 119-124. 

Press, L. (1997, November). Tracking the global diffusion of the Internet. 
Communications of the ACM , 40(1 1), 11-17. 

Press, L., Burkhart, G., Foster, W., Goodman, S., Wolcott, P., & Woodard, J. (1998). An 
Internet diffusion framework. Communications of the ACM , 41(10), 21-26. 

Pritchett, L., & Kaufmann, D. (1998, March). Civil liberties, democracy, and the 

performance of government projects. Finance & Development . Retrieved February 
17, 2003, from http://www.worldbank.org/fandd/english/pdfs/0398/0140398.pdf 

Rey, L. (1998). Multiculturality and communication technologies in Switzerland. 
Electronic Journal of Communication , 8. Retrieved February 17, 2003, from 
http://www.cios.org/getfile 

Research on Internet in Slovenia. (2000). Retrieved February 17, 2003, from http://www 
.ris.org/ict.html 

Rodriguez, F., & Wilson, E. J. (2000, May). Are poor countries losing the information 
revolution? Retrieved February 17, 2003, from http://www.infodev.org/library/ 
WorkingPapers/wilsonrodriguez.doc 



180 
Rogers, E. (1995). Diffusion of innovations (4 th e<±). New York: Free Press. 






Rogerson, K., & Thomas, D. G. (1998). Internet regulation process model: The effect of 
societies, communities, and governments. Journal of Political Communication , 15, 
427-444. 

Roller, L., & Waverman, L. (2001). Telecommunications infrastructure and economic 
development: A simultaneous approach. American Economic Review , 91(4), 909- 
923. 

Romer, P. (1999). What makes technology grow? The Wilson Quarterly , 23(2), 1 1-13. 

Rood, M. (1999). A word about Internet statistics. Telecommunications Policy , 
23(10/11), 687-688. 

Rose, R. (2002). Digital divide or digital diffusion? Transition , 13(4-5), 33-35. 

Ryan, D. (1997). Privatization and competition in telecommunications . Westport, CN: 
Praeger. 

Ryan, B., & Gross, N. C. (1943). The diffusion of hybrid seed corn in two Iowa 
communities. Rural Sociology , 8, 15-24. 

Sadowsky, G. (1993). Network connectivity for developing countries. Communications 
of the ACM , 36(8), 42-47. 

Sale, K. (1999). The triumph of technology. The Ecologist , 29(3), 187-188. 

Sallai, G. (2000, August). Reform and development of Hungarian telecommunications. 
IEEE Communications Magazine , 38(8), 82-87. 

Sen, A. (1999a). Democracy as a universal value. Journal of Democracy , 10(3), 3-17. 

Sen, A. (1999b). Development as freedom . New York: Knopf. 

Severin, W. J., & Tankard, J. W., Jr. (1997). Communication theories: Origins, methods, 
and uses in the mass media (4 th ed.). New York: Longman. 

Shapiro, C, & Varian, H. R. (1999). Information rules: A strategic guide to the network 
economy . Boston, MA: Harvard Business School Press. 

Singh, J. P. (1999). Leapfrogging development? The political economy of 

telecommunications restructuring . Albany, NY: State University of New York 
Press. 

Sokolov, N., & Goldenstein, B. (2000, August). Telecommunications in Russia. IEEE 
Communications Magazine , 38(8), 106- 111. 



181 



Solomon, N. (1998). Internet shopping network: The mailing of cyberspace. Retrieved 
February 17, 2003, from http://www.fair.org/media-beaty981029.html 

Sondergaard, M. (1994). Hofstede's consequences: A study of reviews, citations and 
replications. Organization Studies , 15(3), 447- 456. 

Standage, T. (1998). The Victorian internet: The remarkable story of the telegraph and 
the 19 th century's on-line pioneers . New York: Walker. 

Stevens, J. (1992). Applied multivariate statistics for the social sciences (2 n ed.). 
Hillsdale, NJ: Lawrence Erlbaum. 

Stewart, C. M., Shields, S. F., & Sen, N. (1998). Diversity in on-line discussions: A 
study of cultural and gender differences in listservs. Electronic Journal of 
Communication , 8. Retrieved February 17, 2003, from http://www.cios.org/getfile 

Tanner, E. (1999). Links to the world. Gazette: International Journal for Communication 
Studies , 61(1), 39-57. 

Tarde, G. (1903). The laws of imitation . New York: H. Holt and Company. 

Tayeb, M. (1994). Organizations and national culture: Methodology considered. 
Organization Studies , 15(3), 429-446. 

Tele-haves and have-nots: Developing countries make use of technological innovations. 
(1996, May 18). The Economist , 19-20. 

The cyber challenge. (2000, July 3). Transitions Online . Retrieved February 17, 2003, 
from http://www.tol.cz/julOO/thecyber.html 

The Internet's new borders. (2001, August 11). The Economist , 9-10. 

The new economy. (2000, September 23). The Economist , 5-40. 

Tobin, J. (1958). Estimation of relationships for limited dependent variables. 
Econometrica , 26, 24-36. 

United Nations Development Programme. (1999). Transition 1999: Human development 
report for Central and Eastern Europe and the CIS . New York: UNDP. 

United States Agency for International Development. (2000). Bulgaria assessment: 
Internet environment for economic development. Retrieved February 17, 2003, 
from http://www.usaid.gov/info_technology/ied/reports/bulgaria/environment.html 

United States Census Bureau. (1997, October). Computer use and ownership. Retrieved 
February 17, 2003, from http://www.census.gov/population/www/socdemo/ 
computer.html 



182 

United States Commerce Department. (1998, April). The emergin g digital economy. 
Retrieved February 17, 2003, from http://www.ecommerce.gov/emerging.htm 

United States Internet Council. (2000, September). State of the Internet 2000. Retrieved 
February 17, 2003, from http://www.usic.org/papers/stateoftheinternet2000/ 
intro.html 

Wallraff, B. (2000, November). What global language? The Atlantic Monthly , 52-66. 

Wallsten, S. (1999). An empirical analysis of competition, privatization, and regulation in 
telecommunications markets in Africa and Latin America. World Bank Working 
Paper 2817, Retrieved February 17, 2003, from http://ideas.repec.Org/p/wop/ 
wobago/2 136.html 

Wallsten, S. (2002, March 25). Does sequencing matter? Regulation and privatization in 
telecommunications reforms. World Bank Working Paper 2817, Retrieved 
February 17, 2003, from http://econ.worldbank.org/view.php?type=5&id= 13266 

Weir, T. (1999). Innovators or news hounds? Newspaper Research Journal , 20(4), 62-81. 

Wheatley, J. (1999). World telecommunications economics . London: The Institution of 
Electrical Engineers. 

Wilson, B., Ryder, M., McCahan, J., & Sherry, L. (1996). Cultural assimilation of the 
Internet: A case study. Retrieved February 17, 2003, from http://carbon.cudenver 
.edu/~bwilson/cultass.html 

Winner, L. (1997) Technology today: Utopia or dystopia? Social Research , 64(3), 989- 
1008. 

Winston, B. (1998). Media technology and society: A history: From the telegraph to the 
Internet . London: Routledge. 

Wolcott, P., Press, L., McHenry, W., Goodman, S., & Foster, W. (2001). A framework 
for assessing the global diffusion of the Internet. Journal of the Association for 
Information Systems , 2(6). Retrieved February 17, 2003, from http://www.isns 
.unomaha.edu/isqa/wolcott/GDI/2001_GDI_Framework.htm 

World Bank. (1998). World development indicators . Washington, D.C.: The World Bank. 

World Bank. (2000). World development indicators . Washington, D.C.: The World Bank. 

World Bank. (2001). World development indicators . Washington, D.C.: The World Bank. 

World Intellectual Property Organization. (2001). Primer on electronic commerce and 
intellectual property issues. Retrieved February 17, 2003, from http://ecommerce 
. wipo.int/primer/section 1 .html 



183 



Xavier, P. (2000). Market liberalisation and regulation in Hungary's telecommunications 
sector. Telecommunications Policy , 24(10/11), 807-841. 

Yakovlev, Y. (1989). Flagship of Glasnost. In Cohen S., & vanden Heuvel, K. (eds.) 
Voices of Glasnost: Interviews with Gorbachev's Reformers (pp. 197-212). New 
York: Norton. 

Zook, M. (2000). Internet metrics: Using host and domain counts to map the internet. 
Telecommunications Policy , 24(6/7), 613-620. 

Zook, M. (2002). Zooknic Internet intelligence: Internet users worldwide. Retrieved 
February 17, 2003, from http://www.zooknic.com/Users/index.htm 



BIOGRAPHICAL SKETCH 

Dimitrova received a Master of Arts in journalism and communication from the 
University of Oregon in 1999. She holds a Bachelor of Arts degree in journalism/mass 
communication and political science/international relations from the American University 
in Bulgaria (AUBG). Dimitrova' s professional background is in radio and television 
news. 

Daniela Dimitrova conducts research in the area of new media and the Internet. Her 
research interests include Internet use at both the micro and macro level. She has 
examined the content and design of online media in previous studies, focusing on the use 
of hyperlinks, multimedia and interactivity. In addition, Dimitrova' s research focuses on 
political communication and online media management. 









184 




I certify that I have read this study and that in my opinion it conforms to acceptable 
standards of scholarly presentation and is fully adequate, in scope and quality, as a 
dissertation for the degree of Doctor of Philosophy. 

Sylvjt Chan-Olmsted, Chair 
Associate Professor of Journalism and 
Communications 

I certify that I have read this study and that in my opinion it conforms to acceptable 
standards of scholarly presentation and is fully adequate, in scope and quality, as a 
dissertation for the degree of Doctor of Philosophy .^- 

Kurt Kent 

Professor of Journalism and 
Communications 

I certify that I have read this study and that in my opinion it conforms to acceptable 
standards of scholarly presentation and is fully adequate, in scope and quality, as a 
dissertation for the degree of Doctor of Philosophy. 



f A.: ^ 'Tacm^- 



Melinda McAdams 
Professor of Journalism and 
Communications 



I certify that I have read this study and that in my opinion it conforms to acceptable 
standards of scholarly presentation and is fully adequate, in scope and quality, as a 
dissertation for the degree of Doctor of Philosophy. 

WayneWanta 

Associate Professor of Journalism and 
Communications 

I certify that I have read this study and that in my opinion it conforms to acceptable 
standards of scholarly presentation and is fully adequate, in scope and quality, as a 
dissertation for the degree of Doctor of Philosophy. 




Richard Beilock 

Professor of Food and Resource Economics 



This dissertation was submitted to the Graduate Faculty of the College of 
Journalism and Communications and to the Graduate School and was accepted as partial 
fulfillment of the requirements for the degree of Doctor of Philosophy. 

May 2003 A> 



san, College4)f Journalism and 



Dean, 

Communications 



Dean, Graduate School 













UNIVERSITY OF FLORIDA 



3 1262 08557 1932